{"id":572,"date":"2026-04-14T13:53:02","date_gmt":"2026-04-14T13:53:02","guid":{"rendered":"https:\/\/www.devopsschool.com\/tutorials\/google-cloud-vertex-ai-prediction-tutorial-architecture-pricing-use-cases-and-hands-on-guide-for-ai-and-ml\/"},"modified":"2026-04-14T13:53:02","modified_gmt":"2026-04-14T13:53:02","slug":"google-cloud-vertex-ai-prediction-tutorial-architecture-pricing-use-cases-and-hands-on-guide-for-ai-and-ml","status":"publish","type":"post","link":"https:\/\/www.devopsschool.com\/tutorials\/google-cloud-vertex-ai-prediction-tutorial-architecture-pricing-use-cases-and-hands-on-guide-for-ai-and-ml\/","title":{"rendered":"Google Cloud Vertex AI Prediction Tutorial: Architecture, Pricing, Use Cases, and Hands-On Guide for AI and ML"},"content":{"rendered":"\n<h2 class=\"wp-block-heading\">Category<\/h2>\n\n\n\n<p>AI and ML<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">1. Introduction<\/h2>\n\n\n\n<p>Vertex AI Prediction is the Google Cloud capability for serving machine learning models for <strong>online (real-time)<\/strong> and <strong>batch<\/strong> predictions. It is designed to take a trained model (from Vertex AI training, open-source frameworks, or elsewhere), deploy it behind a managed endpoint, and reliably return predictions at scale with security, monitoring, and operational controls.<\/p>\n\n\n\n<p>In simple terms: you train a model, upload it to Vertex AI, deploy it to an endpoint, and call that endpoint from your app to get predictions\u2014without managing Kubernetes clusters, custom load balancers, or inference servers yourself.<\/p>\n\n\n\n<p>Technically, Vertex AI Prediction is implemented through Vertex AI resources such as <strong>Model<\/strong>, <strong>Endpoint<\/strong>, <strong>DeployedModel<\/strong>, and <strong>BatchPredictionJob<\/strong>, exposed via the Vertex AI API (aiplatform.googleapis.com). For online prediction, you provision compute for inference (CPU\/GPU\/TPU depending on model needs), optionally enable autoscaling, configure traffic splitting, and then send prediction requests to the regional endpoint. For batch prediction, you submit a job that reads input instances from Cloud Storage or BigQuery (depending on supported formats and configuration) and writes predictions back to Cloud Storage or BigQuery.<\/p>\n\n\n\n<p>The problem it solves: getting models into production\u2014securely, reliably, and cost-effectively\u2014while reducing the platform burden of building and operating your own inference stack.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">2. What is Vertex AI Prediction?<\/h2>\n\n\n\n<p><strong>Vertex AI Prediction<\/strong> is the Vertex AI service area in Google Cloud that provides managed model inference for:\n&#8211; <strong>Online prediction<\/strong>: low-latency, request\/response predictions from a deployed endpoint\n&#8211; <strong>Batch prediction<\/strong>: high-throughput offline scoring over large datasets using batch jobs<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Official purpose (what it\u2019s for)<\/h3>\n\n\n\n<p>Vertex AI Prediction exists to operationalize ML models by providing:\n&#8211; Managed endpoints for real-time inference\n&#8211; Batch scoring pipelines without custom infrastructure\n&#8211; Integrated security (IAM), observability (Logging\/Monitoring), and governance (Audit Logs)<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Core capabilities<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Upload models (or reference artifacts) into Vertex AI as <strong>Model<\/strong> resources<\/li>\n<li>Deploy one or more model versions to a single <strong>Endpoint<\/strong><\/li>\n<li>Control traffic between versions (canary, A\/B testing, blue\/green patterns)<\/li>\n<li>Autoscale inference compute (within configured min\/max replica counts)<\/li>\n<li>Run batch prediction jobs for offline scoring<\/li>\n<li>Integrate with Vertex AI features like model monitoring (where applicable) and logging controls<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Major components (key resources)<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Model<\/strong>: a registered model artifact + serving configuration<\/li>\n<li><strong>Endpoint<\/strong>: a regional HTTPS endpoint that hosts one or more deployed models<\/li>\n<li><strong>DeployedModel<\/strong>: a specific model deployment on an endpoint, including machine type and scaling settings<\/li>\n<li><strong>PredictionService API<\/strong>: the API used to call online prediction<\/li>\n<li><strong>BatchPredictionJob<\/strong>: a job resource for offline scoring<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Service type<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Fully managed Google Cloud service (managed control plane and managed serving infrastructure)<\/li>\n<li>You bring model artifacts (and optionally a serving container), Google Cloud runs the inference fleet<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Scope (regional\/global\/project)<\/h3>\n\n\n\n<p>Vertex AI resources are <strong>project-scoped<\/strong> and <strong>location-scoped<\/strong>:\n&#8211; You create Vertex AI Models and Endpoints in a specific <strong>Google Cloud location<\/strong> (often a region such as <code>us-central1<\/code>).\n&#8211; Online prediction requests go to the <strong>regional Vertex AI endpoint<\/strong> for that location.\n&#8211; Data residency and latency depend on the location you choose.<\/p>\n\n\n\n<p>Always verify available locations and feature availability in official docs because some capabilities vary by region and by model type.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">How it fits into the Google Cloud ecosystem<\/h3>\n\n\n\n<p>Vertex AI Prediction sits at the \u201cserving\u201d layer of an ML lifecycle:\n&#8211; Data sources: <strong>BigQuery<\/strong>, <strong>Cloud Storage<\/strong>, <strong>Pub\/Sub<\/strong>\n&#8211; Training: <strong>Vertex AI Training<\/strong>, custom training on <strong>GKE<\/strong>, <strong>Dataproc<\/strong>, or external\n&#8211; Serving: <strong>Vertex AI Prediction<\/strong> (Endpoints and BatchPredictionJob)\n&#8211; Ops: <strong>Cloud Logging<\/strong>, <strong>Cloud Monitoring<\/strong>, <strong>Cloud Trace<\/strong> (where applicable), <strong>Cloud Audit Logs<\/strong>\n&#8211; Security: <strong>IAM<\/strong>, <strong>VPC Service Controls<\/strong>, <strong>Private Service Connect<\/strong>, <strong>Cloud KMS<\/strong> (for key management, where applicable)<\/p>\n\n\n\n<blockquote>\n<p>Naming note (legacy context): Vertex AI is the successor to the older \u201cAI Platform\u201d products. If you find older documentation referring to \u201cAI Platform Prediction,\u201d treat it as legacy and follow the current Vertex AI docs unless you are maintaining an older system.<\/p>\n<\/blockquote>\n\n\n\n<h2 class=\"wp-block-heading\">3. Why use Vertex AI Prediction?<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Business reasons<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Faster time to production<\/strong>: deploy a model as an API without building a custom serving platform<\/li>\n<li><strong>Lower operational overhead<\/strong>: Google Cloud manages scaling, patching, and serving infrastructure<\/li>\n<li><strong>Experimentation support<\/strong>: traffic splitting and multiple deployments per endpoint enable safer releases<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Technical reasons<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Standardized serving<\/strong>: consistent APIs and resource model (Models\/Endpoints\/Jobs) across teams<\/li>\n<li><strong>Supports multiple model types<\/strong>: custom containers, framework-specific approaches, and managed options (depending on model)<\/li>\n<li><strong>Batch + online<\/strong>: use the same model artifacts for both real-time and offline scoring patterns<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Operational reasons<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Autoscaling<\/strong>: scale inference resources within configured bounds<\/li>\n<li><strong>Observability<\/strong>: integrate with Cloud Logging and Cloud Monitoring for latency, errors, and throughput<\/li>\n<li><strong>Versioning and rollout controls<\/strong>: manage model versions and rollouts without redeploying your application<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Security\/compliance reasons<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>IAM-based authorization<\/strong>: control who can deploy and who can invoke endpoints<\/li>\n<li><strong>Auditability<\/strong>: administrative operations are captured in Cloud Audit Logs<\/li>\n<li><strong>Private networking options<\/strong>: Private Service Connect and VPC Service Controls help reduce public exposure and data exfiltration risk<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Scalability\/performance reasons<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Designed to handle real-time prediction workloads with managed capacity and regional routing<\/li>\n<li>Can be configured for higher throughput using larger machine types, accelerators, and multiple replicas<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">When teams should choose it<\/h3>\n\n\n\n<p>Choose Vertex AI Prediction when you need:\n&#8211; A managed, secure prediction endpoint with IAM auth\n&#8211; Repeatable deployments and releases (dev\/stage\/prod)\n&#8211; A supported path for batch scoring at scale\n&#8211; Operational tooling without running your own serving cluster<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">When teams should not choose it<\/h3>\n\n\n\n<p>Consider alternatives when:\n&#8211; You need extremely custom network fronting (WAF, custom auth, custom routing) and want full control\u2014<strong>Cloud Run<\/strong> or <strong>GKE\/KServe<\/strong> may fit better\n&#8211; Your model is small and you already run an app platform where inference can be embedded (e.g., a microservice on Cloud Run) and you want fewer moving parts\n&#8211; You must run inference in an environment not supported by Google Cloud (strict on-prem-only requirement)<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">4. Where is Vertex AI Prediction used?<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Industries<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Retail and e-commerce (recommendations, demand forecasting, fraud)<\/li>\n<li>Finance (risk scoring, fraud detection, credit decisioning support)<\/li>\n<li>Healthcare\/life sciences (triage support, claims classification; subject to compliance constraints)<\/li>\n<li>Manufacturing (predictive maintenance, anomaly detection)<\/li>\n<li>Media\/gaming (content moderation signals, churn prediction)<\/li>\n<li>Logistics (ETA prediction, route optimization scoring)<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Team types<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>ML engineering teams deploying models into production<\/li>\n<li>Platform teams building a shared ML serving layer<\/li>\n<li>Data science teams moving from notebooks to services<\/li>\n<li>DevOps\/SRE teams responsible for reliability, monitoring, and cost controls<\/li>\n<li>Security teams enforcing least privilege and network restrictions<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Workloads<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Low-latency synchronous inference for user-facing apps<\/li>\n<li>High-volume scoring for marketing lists, fraud sweeps, or nightly refreshes<\/li>\n<li>Streaming architectures where online prediction is invoked from a subscriber or microservice<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Architectures<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Microservices calling Vertex AI endpoints<\/li>\n<li>Event-driven scoring (Pub\/Sub \u2192 Cloud Run \u2192 Vertex AI endpoint)<\/li>\n<li>Batch pipelines (BigQuery\/Cloud Storage \u2192 BatchPredictionJob \u2192 BigQuery\/Cloud Storage)<\/li>\n<li>Multi-environment promotion (dev \u2192 staging \u2192 prod) with controlled rollouts<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Production vs dev\/test usage<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Dev\/test<\/strong>: smaller machine types, minimal replicas, limited logging sampling, fast iteration<\/li>\n<li><strong>Production<\/strong>: autoscaling, private connectivity where required, strict IAM boundaries, monitoring\/alerts, deployment automation (CI\/CD), and controlled traffic splitting<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">5. Top Use Cases and Scenarios<\/h2>\n\n\n\n<p>Below are realistic scenarios where Vertex AI Prediction is commonly used.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">1) Real-time fraud risk scoring<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Problem<\/strong>: Evaluate transactions in milliseconds to block suspicious activity.<\/li>\n<li><strong>Why Vertex AI Prediction fits<\/strong>: Managed endpoints, autoscaling, IAM, and predictable latency within a region.<\/li>\n<li><strong>Example<\/strong>: Payment service calls a Vertex AI endpoint with transaction features; response returns risk probability and reason codes.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">2) Customer churn prediction API<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Problem<\/strong>: Customer success tools need churn risk at the moment an agent opens an account.<\/li>\n<li><strong>Why it fits<\/strong>: Low-latency online prediction integrated into CRM workflows.<\/li>\n<li><strong>Example<\/strong>: CRM backend calls Vertex AI Prediction for churn score; UI highlights at-risk customers.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">3) Batch scoring for campaign targeting<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Problem<\/strong>: Score millions of users nightly for next-day campaign segmentation.<\/li>\n<li><strong>Why it fits<\/strong>: BatchPredictionJob handles large offline scoring without standing up clusters.<\/li>\n<li><strong>Example<\/strong>: BigQuery export \u2192 batch prediction \u2192 results loaded back into BigQuery for BI dashboards.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">4) Predictive maintenance scoring<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Problem<\/strong>: Score equipment telemetry to flag likely failures.<\/li>\n<li><strong>Why it fits<\/strong>: Real-time endpoint for immediate alerts; batch for historical re-scoring.<\/li>\n<li><strong>Example<\/strong>: Cloud Run service preprocesses sensor messages and calls the endpoint.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">5) Demand forecasting as a service<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Problem<\/strong>: Internal teams need a consistent forecast API for products\/regions.<\/li>\n<li><strong>Why it fits<\/strong>: Centralized endpoint serving a standard model with controlled rollouts.<\/li>\n<li><strong>Example<\/strong>: Inventory system calls the endpoint daily for product-level forecasts.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">6) Content quality classification in a pipeline<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Problem<\/strong>: Classify uploaded content and route to moderation workflows.<\/li>\n<li><strong>Why it fits<\/strong>: Scales with upload volume; integrates with event-driven architectures.<\/li>\n<li><strong>Example<\/strong>: Object finalize event \u2192 Cloud Run \u2192 Vertex endpoint \u2192 store label in Firestore\/BigQuery.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">7) Anomaly detection for monitoring signals<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Problem<\/strong>: Detect anomalies in metrics or logs to reduce alert fatigue.<\/li>\n<li><strong>Why it fits<\/strong>: Endpoint can be called from a monitoring pipeline; batch scoring for retrospectives.<\/li>\n<li><strong>Example<\/strong>: Dataflow aggregates signals and calls Vertex AI Prediction for anomaly score.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">8) Personalized ranking features (near-real time)<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Problem<\/strong>: Generate ranking scores for content feeds.<\/li>\n<li><strong>Why it fits<\/strong>: Supports rapid iteration and controlled rollouts via traffic splitting.<\/li>\n<li><strong>Example<\/strong>: Feed service calls endpoint for each candidate set; uses score to rank.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">9) Document classification in enterprise workflows<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Problem<\/strong>: Classify incoming PDFs\/forms for routing.<\/li>\n<li><strong>Why it fits<\/strong>: Standard endpoint interface and strong IAM for internal applications.<\/li>\n<li><strong>Example<\/strong>: Internal ingestion service extracts text and calls endpoint for document type label.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">10) Model version canary testing<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Problem<\/strong>: Deploy a new model safely and compare it to the current model.<\/li>\n<li><strong>Why it fits<\/strong>: Multiple deployed models per endpoint with traffic splits.<\/li>\n<li><strong>Example<\/strong>: Route 5% traffic to new model; compare latency and prediction distribution before full cutover.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">11) Cost-controlled shared inference for multiple apps<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Problem<\/strong>: Multiple applications need predictions, but separate serving stacks are expensive.<\/li>\n<li><strong>Why it fits<\/strong>: Centralized endpoints + IAM and per-environment controls.<\/li>\n<li><strong>Example<\/strong>: Shared endpoint in prod; separate endpoints in staging\/dev with smaller replicas.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">12) Regulated environment inference with restricted access<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Problem<\/strong>: Predictions must stay within controlled perimeters and auditable access patterns.<\/li>\n<li><strong>Why it fits<\/strong>: IAM + Audit Logs + VPC Service Controls and private connectivity options.<\/li>\n<li><strong>Example<\/strong>: Private endpoint + org policy constraints + restricted service accounts for invocation.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">6. Core Features<\/h2>\n\n\n\n<p>This section focuses on the core Vertex AI Prediction capabilities used for real deployments.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Online prediction (Endpoints)<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>What it does<\/strong>: Hosts one or more deployed models behind a regional HTTPS endpoint. Clients send prediction requests and receive responses synchronously.<\/li>\n<li><strong>Why it matters<\/strong>: Enables low-latency inference for user-facing applications and services.<\/li>\n<li><strong>Practical benefit<\/strong>: You avoid operating your own inference servers and can standardize deployment practices.<\/li>\n<li><strong>Limitations\/caveats<\/strong>:<\/li>\n<li>You pay for deployed compute while it\u2019s running (even if idle).<\/li>\n<li>Latency depends on region, machine type, model size, and request payload.<\/li>\n<li>Public endpoint access requires careful IAM and network controls; private options may require extra setup.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Batch prediction (BatchPredictionJob)<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>What it does<\/strong>: Runs offline scoring jobs over large datasets and writes outputs to a destination (commonly Cloud Storage, sometimes BigQuery depending on configuration and supported formats).<\/li>\n<li><strong>Why it matters<\/strong>: Many ML workloads are offline (nightly scoring, backfills, large analytics).<\/li>\n<li><strong>Practical benefit<\/strong>: Scale scoring without standing up ephemeral clusters or custom batch infrastructure.<\/li>\n<li><strong>Limitations\/caveats<\/strong>:<\/li>\n<li>Job startup time can be higher than online.<\/li>\n<li>Output formatting and input schema must follow supported formats.<\/li>\n<li>Costs depend on job compute and runtime; monitor job size carefully.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Model Registry integration (Models as first-class resources)<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>What it does<\/strong>: Registers model artifacts, metadata, and serving configuration as a Vertex AI Model resource.<\/li>\n<li><strong>Why it matters<\/strong>: Centralizes models for governance, reuse, and controlled promotions.<\/li>\n<li><strong>Practical benefit<\/strong>: Enables repeatable deployments and consistent permissioning.<\/li>\n<li><strong>Limitations\/caveats<\/strong>:<\/li>\n<li>Model artifacts must be accessible to Vertex AI (typically via Cloud Storage or container image registry).<\/li>\n<li>Regional scoping means you must plan where models live.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Multiple models per endpoint + traffic splitting<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>What it does<\/strong>: Deploy multiple model versions to one endpoint and split traffic by percentage.<\/li>\n<li><strong>Why it matters<\/strong>: Enables safer releases and experimentation.<\/li>\n<li><strong>Practical benefit<\/strong>: Canary releases, A\/B tests, and rollback without changing client code.<\/li>\n<li><strong>Limitations\/caveats<\/strong>:<\/li>\n<li>Split is by request percentage, not necessarily by user\/session unless your app routes requests accordingly.<\/li>\n<li>Comparing models may require separate logging\/analysis pipelines.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Autoscaling (replica-based)<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>What it does<\/strong>: Scales deployed model replicas between configured min\/max counts based on load.<\/li>\n<li><strong>Why it matters<\/strong>: Handles variable traffic without manual capacity planning.<\/li>\n<li><strong>Practical benefit<\/strong>: Improves cost efficiency relative to overprovisioning.<\/li>\n<li><strong>Limitations\/caveats<\/strong>:<\/li>\n<li>You still pay for the minimum replicas at all times.<\/li>\n<li>Scaling behavior is bounded by configured max replicas and quotas.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Prediction request\/response logging controls<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>What it does<\/strong>: Allows enabling logs (often with sampling) for prediction requests and responses.<\/li>\n<li><strong>Why it matters<\/strong>: Supports debugging, auditability, and monitoring pipelines.<\/li>\n<li><strong>Practical benefit<\/strong>: Trace issues and analyze model inputs\/outputs patterns.<\/li>\n<li><strong>Limitations\/caveats<\/strong>:<\/li>\n<li>Logging sensitive data can create compliance risk; sanitize or avoid logging PII.<\/li>\n<li>Logging can add cost (Cloud Logging ingestion\/storage) and operational overhead.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Private connectivity options (Private Service Connect) and perimeter controls (VPC Service Controls)<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>What it does<\/strong>:<\/li>\n<li>Private Service Connect (PSC) can provide private access paths to Google APIs, including Vertex AI, depending on current support.<\/li>\n<li>VPC Service Controls (VPC-SC) can restrict data exfiltration by defining service perimeters.<\/li>\n<li><strong>Why it matters<\/strong>: Reduces exposure and strengthens security posture.<\/li>\n<li><strong>Practical benefit<\/strong>: Keep inference calls private (network-wise) and reduce data leakage pathways.<\/li>\n<li><strong>Limitations\/caveats<\/strong>:<\/li>\n<li>Setup is more complex and requires network\/security coordination.<\/li>\n<li>Validate current PSC and VPC-SC compatibility for your exact location and setup in official docs.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Explainability (Explainable AI) for supported models (where applicable)<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>What it does<\/strong>: Provides feature attributions for predictions for supported model types\/configurations.<\/li>\n<li><strong>Why it matters<\/strong>: Improves interpretability and supports compliance or stakeholder trust requirements.<\/li>\n<li><strong>Practical benefit<\/strong>: Debug model behavior and produce explanations for downstream use.<\/li>\n<li><strong>Limitations\/caveats<\/strong>:<\/li>\n<li>Not all model types or custom containers support integrated explanations automatically.<\/li>\n<li>Explanations can increase latency and cost.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">IAM integration and service accounts for invocation<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>What it does<\/strong>: Uses Google Cloud IAM to authorize prediction calls and admin operations.<\/li>\n<li><strong>Why it matters<\/strong>: Centralized access control and auditability.<\/li>\n<li><strong>Practical benefit<\/strong>: Use least-privilege service accounts per application\/environment.<\/li>\n<li><strong>Limitations\/caveats<\/strong>:<\/li>\n<li>Misconfigured IAM can unintentionally allow broad access to endpoints.<\/li>\n<li>Cross-project invocation requires explicit IAM grants and careful design.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">7. Architecture and How It Works<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">High-level service architecture<\/h3>\n\n\n\n<p>At a high level, Vertex AI Prediction has two primary execution paths:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>\n<p><strong>Online prediction path<\/strong>\n&#8211; You upload\/register a model in Vertex AI.\n&#8211; You create an endpoint in a region.\n&#8211; You deploy the model to the endpoint with chosen compute (machine type, accelerators, replicas).\n&#8211; Clients call <code>:predict<\/code> on the endpoint\u2019s regional API URL.\n&#8211; Vertex AI routes the request to a replica running your serving container and returns predictions.<\/p>\n<\/li>\n<li>\n<p><strong>Batch prediction path<\/strong>\n&#8211; You create a batch prediction job specifying:\n  &#8211; Model to use\n  &#8211; Input source (often Cloud Storage; sometimes BigQuery depending on workflow)\n  &#8211; Output destination\n  &#8211; Compute configuration\n&#8211; Vertex AI runs the job and writes outputs to the destination.<\/p>\n<\/li>\n<\/ol>\n\n\n\n<h3 class=\"wp-block-heading\">Request\/data\/control flow<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Control plane<\/strong>: Model uploads, endpoint creation, deployment operations, IAM, and configurations are control-plane actions performed via Vertex AI API and logged in Cloud Audit Logs.<\/li>\n<li><strong>Data plane<\/strong>: Prediction payloads are data-plane operations. Prediction calls are authenticated and authorized; payload handling is subject to your logging configuration and security controls.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Integrations with related Google Cloud services<\/h3>\n\n\n\n<p>Common integrations include:\n&#8211; <strong>Cloud Storage<\/strong>: model artifacts, batch input\/output, logs export pipelines\n&#8211; <strong>Artifact Registry<\/strong>: storing custom prediction container images\n&#8211; <strong>Cloud Build<\/strong>: building and publishing serving containers\n&#8211; <strong>BigQuery<\/strong>: storing features, offline scoring outputs, analytics\n&#8211; <strong>Pub\/Sub<\/strong>: event triggers for scoring workflows\n&#8211; <strong>Cloud Run \/ GKE<\/strong>: application services that call Vertex endpoints\n&#8211; <strong>Cloud Monitoring &amp; Cloud Logging<\/strong>: metrics, logs, alerts, debugging\n&#8211; <strong>Cloud Audit Logs<\/strong>: governance and compliance evidence\n&#8211; <strong>Cloud KMS<\/strong>: key management for related resources; verify exact encryption configuration requirements in docs<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Dependency services (typical)<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Vertex AI API enabled in the project: <code>aiplatform.googleapis.com<\/code><\/li>\n<li>Artifact Registry API for container-based serving: <code>artifactregistry.googleapis.com<\/code><\/li>\n<li>Cloud Build API for building images: <code>cloudbuild.googleapis.com<\/code><\/li>\n<li>Cloud Storage for artifact hosting: <code>storage.googleapis.com<\/code><\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Security\/authentication model<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Clients authenticate using:<\/li>\n<li><strong>Service account tokens<\/strong> (most common for workloads)<\/li>\n<li><strong>User credentials<\/strong> (for developer testing)<\/li>\n<li>Authorization is enforced by IAM permissions on Vertex AI resources (project-level and resource-level).<\/li>\n<li>Administrative and deployment actions are logged in Cloud Audit Logs.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Networking model<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Default online prediction calls use public Google APIs endpoints (HTTPS to <code>*.googleapis.com<\/code>) with IAM-based auth.<\/li>\n<li>For private access patterns, organizations often combine:<\/li>\n<li>Private access to Google APIs (e.g., Private Google Access)<\/li>\n<li>Private Service Connect (where supported for the relevant Google APIs and configuration)<\/li>\n<li>VPC Service Controls service perimeters to reduce exfiltration risk<\/li>\n<\/ul>\n\n\n\n<p>Always validate your required network pattern with the latest official docs because private connectivity options can have specific prerequisites and constraints.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Monitoring\/logging\/governance considerations<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Enable Cloud Monitoring dashboards\/alerts for latency, error rate, and throughput.<\/li>\n<li>Decide whether to log prediction request\/response payloads; if you do, apply strict data minimization and sampling.<\/li>\n<li>Use labels\/tags and a consistent naming convention for endpoints, models, and deployments.<\/li>\n<li>Use separate projects (or at least separate environments) for dev\/stage\/prod.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Simple architecture diagram (Mermaid)<\/h3>\n\n\n\n<pre><code class=\"language-mermaid\">flowchart LR\n  A[Client app\\nCloud Run \/ VM \/ On-prem] --&gt;|HTTPS + IAM token| B[Vertex AI Endpoint\\n(online prediction)]\n  B --&gt; C[Deployed Model Replica(s)\\nServing container]\n  C --&gt; B\n  B --&gt; A\n<\/code><\/pre>\n\n\n\n<h3 class=\"wp-block-heading\">Production-style architecture diagram (Mermaid)<\/h3>\n\n\n\n<pre><code class=\"language-mermaid\">flowchart TB\n  subgraph VPC[Customer VPC]\n    CR[Cloud Run service\\n(or GKE service)]\n    PS[Pub\/Sub subscription\\n(optional)]\n    BQ[(BigQuery\\nfeatures + analytics)]\n  end\n\n  subgraph Vertex[Vertex AI (regional)]\n    EP[Vertex AI Endpoint]\n    DM1[DeployedModel v1\\nmin\/max replicas]\n    DM2[DeployedModel v2\\ncanary]\n  end\n\n  subgraph Platform[Platform Services]\n    AR[(Artifact Registry\\nServing image)]\n    GCS[(Cloud Storage\\nModel artifacts + batch I\/O)]\n    CL[Cloud Logging]\n    CM[Cloud Monitoring]\n    CAL[Cloud Audit Logs]\n  end\n\n  CR --&gt;|predict calls| EP\n  EP --&gt;|traffic split| DM1\n  EP --&gt;|traffic split| DM2\n\n  AR --&gt; DM1\n  AR --&gt; DM2\n  GCS --&gt; Vertex\n  Vertex --&gt; CL\n  Vertex --&gt; CM\n  Vertex --&gt; CAL\n\n  PS --&gt; CR\n  BQ &lt;--&gt; CR\n<\/code><\/pre>\n\n\n\n<h2 class=\"wp-block-heading\">8. Prerequisites<\/h2>\n\n\n\n<p>Before you start, ensure you have the following.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Account\/project\/billing<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>A <strong>Google Cloud project<\/strong> with <strong>billing enabled<\/strong><\/li>\n<li>Permission to enable APIs and create resources<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Required APIs<\/h3>\n\n\n\n<p>Enable (at minimum):\n&#8211; Vertex AI API: <code>aiplatform.googleapis.com<\/code>\n&#8211; Cloud Storage API: <code>storage.googleapis.com<\/code>\n&#8211; Artifact Registry API: <code>artifactregistry.googleapis.com<\/code>\n&#8211; Cloud Build API: <code>cloudbuild.googleapis.com<\/code><\/p>\n\n\n\n<h3 class=\"wp-block-heading\">IAM permissions \/ roles<\/h3>\n\n\n\n<p>For a hands-on lab, the simplest is a broad role set. For production, you should use least privilege.<\/p>\n\n\n\n<p>Common roles for the lab (choose the minimum that works in your org):\n&#8211; <code>roles\/aiplatform.admin<\/code> (Vertex AI Admin) for managing models\/endpoints\n&#8211; <code>roles\/storage.admin<\/code> (or narrower) for bucket creation and object access\n&#8211; <code>roles\/artifactregistry.admin<\/code> (or narrower) for repository and image push\n&#8211; <code>roles\/cloudbuild.builds.editor<\/code> to run builds<\/p>\n\n\n\n<p>Production least-privilege typically separates:\n&#8211; Model deployers (CI\/CD) vs. model invokers (applications)\n&#8211; Artifact Registry writers vs. readers\n&#8211; Endpoint admins vs. endpoint users<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Tools<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Cloud Shell<\/strong> (recommended for this lab), or local machine with:<\/li>\n<li><code>gcloud<\/code> CLI (latest available)<\/li>\n<li>Docker (if building locally; Cloud Build can avoid local Docker)<\/li>\n<li>Optional: Python 3.10+ for local testing (Cloud Shell includes Python)<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Region availability<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Pick a Vertex AI supported region such as <code>us-central1<\/code>.<\/li>\n<li>Ensure the region supports the features you plan to use (some features are region-dependent). Verify in official docs.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Quotas and limits<\/h3>\n\n\n\n<p>You may hit quotas for:\n&#8211; Number of endpoints per region\n&#8211; Deployed nodes\/CPUs\/GPUs\n&#8211; Requests per minute\n&#8211; Artifact Registry storage\n&#8211; Cloud Build concurrency<\/p>\n\n\n\n<p>Check quotas in the Google Cloud Console:\n&#8211; <strong>IAM &amp; Admin \u2192 Quotas<\/strong> (or search \u201cQuotas\u201d)\n&#8211; Filter for \u201cVertex AI\u201d and your chosen region<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Prerequisite services (practical)<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>A Cloud Storage bucket for artifacts<\/li>\n<li>An Artifact Registry repository to store your prediction container<\/li>\n<li>A service account for production invocation (recommended)<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">9. Pricing \/ Cost<\/h2>\n\n\n\n<p>Vertex AI pricing is usage-based and depends heavily on how you serve predictions (online vs batch), the compute you choose, and which optional features you enable.<\/p>\n\n\n\n<p>Always confirm the latest SKUs and regional pricing here:\n&#8211; Official pricing page: https:\/\/cloud.google.com\/vertex-ai\/pricing\n&#8211; Pricing calculator: https:\/\/cloud.google.com\/products\/calculator<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Pricing dimensions (typical)<\/h3>\n\n\n\n<h4 class=\"wp-block-heading\">Online prediction (Endpoints)<\/h4>\n\n\n\n<p>Common cost dimensions include:\n&#8211; <strong>Deployed compute<\/strong> billed by time (for example, node\/replica hours) based on:\n  &#8211; Machine type (CPU\/memory)\n  &#8211; Number of replicas (min\/max; you pay at least the minimum)\n  &#8211; Accelerators (GPUs) if used\n&#8211; <strong>Optional logging\/monitoring ingestion costs<\/strong> in Cloud Logging\/Monitoring (separate products)\n&#8211; <strong>Network egress<\/strong> (if clients are outside the region or outside Google Cloud)<\/p>\n\n\n\n<p>Note: The exact billing units and SKUs can change; verify \u201cOnline prediction\u201d SKUs on the pricing page.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Batch prediction<\/h4>\n\n\n\n<p>Common cost dimensions include:\n&#8211; <strong>Compute resources<\/strong> consumed by the batch job (CPU\/GPU and duration)\n&#8211; <strong>Storage I\/O<\/strong> and <strong>Cloud Storage<\/strong> costs for reading inputs\/writing outputs\n&#8211; <strong>BigQuery<\/strong> costs if you use BigQuery as a source\/sink in your pipeline (storage + query + extract\/load)\n&#8211; <strong>Network egress<\/strong> if outputs leave the region\/cloud<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Indirect\/hidden costs to plan for<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Always-on minimum replicas<\/strong> for endpoints (the most common surprise)<\/li>\n<li><strong>Cloud Logging<\/strong> request\/response payload logging volume (can be significant)<\/li>\n<li><strong>Artifact Registry<\/strong> storage for container images<\/li>\n<li><strong>Cloud Storage<\/strong> for model artifacts and batch outputs<\/li>\n<li><strong>Cross-region traffic<\/strong> between your application and the endpoint<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Free tier<\/h3>\n\n\n\n<p>Google Cloud sometimes offers free tiers for certain products, but Vertex AI Prediction is generally <strong>not \u201cfree\u201d<\/strong> once you deploy dedicated compute. Any promotional credits or free usage should be verified in your billing account and the official pricing pages.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Cost drivers (what most affects your bill)<\/h3>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>Machine type and number of replicas<\/strong> (online)<\/li>\n<li><strong>Replica uptime<\/strong> (online endpoints accrue cost while running)<\/li>\n<li><strong>Accelerator selection<\/strong> (GPUs can increase cost dramatically)<\/li>\n<li><strong>Batch job size and duration<\/strong> (batch)<\/li>\n<li><strong>Logging level<\/strong> (request\/response logging)<\/li>\n<li><strong>Egress and cross-region designs<\/strong><\/li>\n<\/ol>\n\n\n\n<h3 class=\"wp-block-heading\">How to optimize cost<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Use the <strong>smallest machine type<\/strong> that meets latency and throughput requirements.<\/li>\n<li>Set <strong>min replicas<\/strong> to the lowest safe value; consider separate endpoints for dev\/test with smaller capacity.<\/li>\n<li>Use <strong>autoscaling<\/strong> with realistic max replicas to cap costs.<\/li>\n<li>Limit prediction payload logging; use <strong>sampling<\/strong> and log only what you need.<\/li>\n<li>Prefer <strong>same-region<\/strong> deployment: run your calling service (Cloud Run\/GKE) in the same region as the Vertex endpoint.<\/li>\n<li>For offline scoring, use <strong>batch prediction<\/strong> instead of keeping an online endpoint running for occasional bulk scoring.<\/li>\n<li>Consider <strong>turning off endpoints<\/strong> (undeploy) when not needed in dev environments.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Example low-cost starter estimate (conceptual)<\/h3>\n\n\n\n<p>A minimal dev endpoint typically includes:\n&#8211; 1 deployed replica on a small CPU machine type\n&#8211; Low traffic\n&#8211; Limited logging<\/p>\n\n\n\n<p>Your primary cost will be <strong>replica uptime<\/strong> (node hours) plus minimal storage and logging. Exact numbers vary by region and machine type\u2014use the pricing calculator and verify SKUs on the Vertex AI pricing page.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Example production cost considerations (conceptual)<\/h3>\n\n\n\n<p>In production, costs often come from:\n&#8211; Multiple replicas (high availability and throughput)\n&#8211; Larger machines and\/or GPUs\n&#8211; Increased logging\/monitoring volume\n&#8211; Separate staging and production endpoints\n&#8211; Continuous batch scoring jobs<\/p>\n\n\n\n<p>A common pattern is to baseline monthly cost by calculating:\n&#8211; <code>(min replicas) \u00d7 (machine hourly rate) \u00d7 (hours\/month)<\/code><br\/>\nand then add headroom for autoscaling, logging, and any accelerators.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">10. Step-by-Step Hands-On Tutorial<\/h2>\n\n\n\n<p>This lab deploys a small, real model behind a Vertex AI endpoint using a <strong>custom prediction container<\/strong> stored in Artifact Registry. The container hosts a simple scikit-learn model trained on the classic Iris dataset.<\/p>\n\n\n\n<p>This approach is practical and avoids relying on framework prebuilt container image URIs that can change over time.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Objective<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Build and push a custom prediction container to Artifact Registry<\/li>\n<li>Upload the container as a Vertex AI Model<\/li>\n<li>Create a Vertex AI Endpoint and deploy the model<\/li>\n<li>Call <code>:predict<\/code> and get real predictions<\/li>\n<li>Validate logs\/metrics basics<\/li>\n<li>Clean up all resources to stop charges<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Lab Overview<\/h3>\n\n\n\n<p>You will create:\n&#8211; An Artifact Registry Docker repository\n&#8211; A Cloud Storage bucket (optional but common in real workflows)\n&#8211; A custom container image that implements <code>\/health<\/code> and <code>\/predict<\/code>\n&#8211; A Vertex AI Model resource\n&#8211; A Vertex AI Endpoint and a DeployedModel\n&#8211; A test prediction request using <code>curl<\/code><\/p>\n\n\n\n<p><strong>Expected outcome<\/strong>: A working Vertex AI Prediction endpoint returning an Iris species prediction (e.g., <code>setosa<\/code>, <code>versicolor<\/code>, <code>virginica<\/code>) for sample measurements.<\/p>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h3 class=\"wp-block-heading\">Step 1: Set project and region, and enable APIs<\/h3>\n\n\n\n<p>In <strong>Cloud Shell<\/strong>, run:<\/p>\n\n\n\n<pre><code class=\"language-bash\">PROJECT_ID=\"$(gcloud config get-value project)\"\nREGION=\"us-central1\"\n\ngcloud config set ai\/region \"$REGION\"\necho \"Project: $PROJECT_ID\"\necho \"Region:  $REGION\"\n<\/code><\/pre>\n\n\n\n<p>Enable required APIs:<\/p>\n\n\n\n<pre><code class=\"language-bash\">gcloud services enable \\\n  aiplatform.googleapis.com \\\n  artifactregistry.googleapis.com \\\n  cloudbuild.googleapis.com \\\n  storage.googleapis.com\n<\/code><\/pre>\n\n\n\n<p><strong>Expected outcome<\/strong>: APIs enabled successfully (may take a minute). If you see permission errors, you need additional IAM permissions to enable services.<\/p>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h3 class=\"wp-block-heading\">Step 2: Create an Artifact Registry repository<\/h3>\n\n\n\n<p>Create a Docker repository in the same region as your Vertex AI resources:<\/p>\n\n\n\n<pre><code class=\"language-bash\">REPO=\"vertex-prediction-lab\"\ngcloud artifacts repositories create \"$REPO\" \\\n  --repository-format=docker \\\n  --location=\"$REGION\" \\\n  --description=\"Vertex AI Prediction lab repo\"\n<\/code><\/pre>\n\n\n\n<p>Configure Docker authentication for Artifact Registry:<\/p>\n\n\n\n<pre><code class=\"language-bash\">gcloud auth configure-docker \"${REGION}-docker.pkg.dev\"\n<\/code><\/pre>\n\n\n\n<p><strong>Expected outcome<\/strong>: Repository created and Docker auth configured.<\/p>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h3 class=\"wp-block-heading\">Step 3: (Optional but recommended) Create a Cloud Storage bucket for artifacts<\/h3>\n\n\n\n<p>Even though this lab serves from a container image, many real deployments store model artifacts in Cloud Storage.<\/p>\n\n\n\n<p>Bucket names must be globally unique:<\/p>\n\n\n\n<pre><code class=\"language-bash\">BUCKET=\"gs:\/\/${PROJECT_ID}-vertex-prediction-lab\"\ngsutil mb -l \"$REGION\" \"$BUCKET\"\n<\/code><\/pre>\n\n\n\n<p><strong>Expected outcome<\/strong>: Bucket created.<\/p>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h3 class=\"wp-block-heading\">Step 4: Create the custom prediction container code<\/h3>\n\n\n\n<p>Create a working directory:<\/p>\n\n\n\n<pre><code class=\"language-bash\">mkdir -p ~\/vertex-ai-prediction-lab\ncd ~\/vertex-ai-prediction-lab\n<\/code><\/pre>\n\n\n\n<p>Create <code>app.py<\/code>:<\/p>\n\n\n\n<pre><code class=\"language-python\">import os\nfrom fastapi import FastAPI\nfrom pydantic import BaseModel\nfrom typing import Any, Dict, List, Optional\n\nimport numpy as np\nfrom sklearn.datasets import load_iris\nfrom sklearn.linear_model import LogisticRegression\n\napp = FastAPI(title=\"Vertex AI Prediction - Iris Demo\")\n\n# Train a small model at container start for demo purposes.\n# In production, you would typically load a serialized model artifact.\niris = load_iris()\nX = iris[\"data\"]\ny = iris[\"target\"]\ntarget_names = iris[\"target_names\"]\n\nmodel = LogisticRegression(max_iter=200)\nmodel.fit(X, y)\n\nclass PredictRequest(BaseModel):\n    instances: List[Dict[str, Any]]\n    parameters: Optional[Dict[str, Any]] = None\n\n@app.get(\"\/health\")\ndef health():\n    return {\"status\": \"ok\"}\n\n@app.post(\"\/predict\")\ndef predict(req: PredictRequest):\n    # Expect each instance to provide four numeric features.\n    # Accept either named features or list-style \"features\".\n    feature_rows = []\n    for inst in req.instances:\n        if \"features\" in inst:\n            row = inst[\"features\"]\n        else:\n            # Named keys for clarity\n            row = [\n                inst[\"sepal_length\"],\n                inst[\"sepal_width\"],\n                inst[\"petal_length\"],\n                inst[\"petal_width\"],\n            ]\n        feature_rows.append(row)\n\n    arr = np.array(feature_rows, dtype=float)\n    probs = model.predict_proba(arr)\n    preds = model.predict(arr)\n\n    results = []\n    for i in range(len(preds)):\n        results.append({\n            \"class_id\": int(preds[i]),\n            \"class_name\": str(target_names[preds[i]]),\n            \"probabilities\": probs[i].tolist()\n        })\n\n    # Vertex AI expects a top-level \"predictions\" field for common patterns.\n    return {\"predictions\": results}\n\nif __name__ == \"__main__\":\n    import uvicorn\n    port = int(os.environ.get(\"AIP_HTTP_PORT\", \"8080\"))\n    uvicorn.run(app, host=\"0.0.0.0\", port=port)\n<\/code><\/pre>\n\n\n\n<p>Create <code>requirements.txt<\/code>:<\/p>\n\n\n\n<pre><code class=\"language-text\">fastapi==0.111.0\nuvicorn[standard]==0.30.1\nscikit-learn==1.5.1\nnumpy==2.0.1\n<\/code><\/pre>\n\n\n\n<p>Create <code>Dockerfile<\/code>:<\/p>\n\n\n\n<pre><code class=\"language-dockerfile\">FROM python:3.11-slim\n\nWORKDIR \/app\n\nCOPY requirements.txt .\nRUN pip install --no-cache-dir -r requirements.txt\n\nCOPY app.py .\n\n# Vertex AI sets AIP_HTTP_PORT (default 8080). Expose 8080 for clarity.\nEXPOSE 8080\n\nCMD [\"python\", \"app.py\"]\n<\/code><\/pre>\n\n\n\n<p><strong>Expected outcome<\/strong>: You have a small FastAPI app with <code>\/health<\/code> and <code>\/predict<\/code>.<\/p>\n\n\n\n<blockquote>\n<p>Why this works: Vertex AI can route requests to your container as long as it listens on the expected port and your deployment specifies the health and predict routes.<\/p>\n<\/blockquote>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h3 class=\"wp-block-heading\">Step 5: Build and push the container image using Cloud Build<\/h3>\n\n\n\n<p>Set your image URI:<\/p>\n\n\n\n<pre><code class=\"language-bash\">IMAGE=\"${REGION}-docker.pkg.dev\/${PROJECT_ID}\/${REPO}\/iris-fastapi:1\"\necho \"$IMAGE\"\n<\/code><\/pre>\n\n\n\n<p>Build and push:<\/p>\n\n\n\n<pre><code class=\"language-bash\">gcloud builds submit --tag \"$IMAGE\" .\n<\/code><\/pre>\n\n\n\n<p><strong>Expected outcome<\/strong>: Build succeeds and the image appears in Artifact Registry.<\/p>\n\n\n\n<p>If the build fails due to permissions, you may need:\n&#8211; Cloud Build service account permissions to write to Artifact Registry\n&#8211; Or you may need to grant Artifact Registry Writer to the Cloud Build service account for this repo<\/p>\n\n\n\n<p>Verify the image exists:<\/p>\n\n\n\n<pre><code class=\"language-bash\">gcloud artifacts docker images list \"${REGION}-docker.pkg.dev\/${PROJECT_ID}\/${REPO}\"\n<\/code><\/pre>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h3 class=\"wp-block-heading\">Step 6: Upload the model to Vertex AI as a container-based model<\/h3>\n\n\n\n<p>Upload the model referencing your serving container image.<\/p>\n\n\n\n<pre><code class=\"language-bash\">MODEL_DISPLAY_NAME=\"iris-fastapi-model\"\n\ngcloud ai models upload \\\n  --region=\"$REGION\" \\\n  --display-name=\"$MODEL_DISPLAY_NAME\" \\\n  --container-image-uri=\"$IMAGE\" \\\n  --container-predict-route=\"\/predict\" \\\n  --container-health-route=\"\/health\" \\\n  --container-ports=\"8080\"\n<\/code><\/pre>\n\n\n\n<p><strong>Expected outcome<\/strong>: Command returns a model resource name like:\n<code>projects\/PROJECT\/locations\/REGION\/models\/MODEL_ID<\/code><\/p>\n\n\n\n<p>Store the model ID:<\/p>\n\n\n\n<pre><code class=\"language-bash\">MODEL_ID=\"$(gcloud ai models list --region=\"$REGION\" --filter=\"displayName=$MODEL_DISPLAY_NAME\" --format=\"value(name)\" | head -n 1)\"\necho \"Model resource: $MODEL_ID\"\n<\/code><\/pre>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h3 class=\"wp-block-heading\">Step 7: Create an endpoint<\/h3>\n\n\n\n<p>Create a Vertex AI Endpoint:<\/p>\n\n\n\n<pre><code class=\"language-bash\">ENDPOINT_DISPLAY_NAME=\"iris-endpoint\"\n\ngcloud ai endpoints create \\\n  --region=\"$REGION\" \\\n  --display-name=\"$ENDPOINT_DISPLAY_NAME\"\n<\/code><\/pre>\n\n\n\n<p>Get the endpoint ID:<\/p>\n\n\n\n<pre><code class=\"language-bash\">ENDPOINT_ID=\"$(gcloud ai endpoints list --region=\"$REGION\" --filter=\"displayName=$ENDPOINT_DISPLAY_NAME\" --format=\"value(name)\" | head -n 1)\"\necho \"Endpoint resource: $ENDPOINT_ID\"\n<\/code><\/pre>\n\n\n\n<p><strong>Expected outcome<\/strong>: You have an endpoint resource ready for deployment.<\/p>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h3 class=\"wp-block-heading\">Step 8: Deploy the model to the endpoint<\/h3>\n\n\n\n<p>Deploy the model using a small machine type. Machine type availability can vary; <code>n1-standard-2<\/code> is a common baseline. If your project\/region doesn\u2019t support it, choose an available small CPU machine type in the console and substitute it here.<\/p>\n\n\n\n<pre><code class=\"language-bash\">DEPLOYED_MODEL_DISPLAY_NAME=\"iris-deployed-v1\"\n\ngcloud ai endpoints deploy-model \"$ENDPOINT_ID\" \\\n  --region=\"$REGION\" \\\n  --model=\"$MODEL_ID\" \\\n  --display-name=\"$DEPLOYED_MODEL_DISPLAY_NAME\" \\\n  --machine-type=\"n1-standard-2\" \\\n  --min-replica-count=1 \\\n  --max-replica-count=1 \\\n  --traffic-split=0=100\n<\/code><\/pre>\n\n\n\n<p><strong>Expected outcome<\/strong>:\n&#8211; Deployment may take several minutes.\n&#8211; When complete, the endpoint has one deployed model receiving 100% of traffic.<\/p>\n\n\n\n<p>Verify deployment:<\/p>\n\n\n\n<pre><code class=\"language-bash\">gcloud ai endpoints describe \"$ENDPOINT_ID\" --region=\"$REGION\"\n<\/code><\/pre>\n\n\n\n<p>Look for <code>deployedModels<\/code> in the output.<\/p>\n\n\n\n<blockquote>\n<p>Cost note: from this point on, you are paying for the deployed replica while it is running. Complete validation and cleanup when done.<\/p>\n<\/blockquote>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h3 class=\"wp-block-heading\">Step 9: Make an online prediction request<\/h3>\n\n\n\n<p>Create a JSON request file:<\/p>\n\n\n\n<pre><code class=\"language-bash\">cat &gt; request.json &lt;&lt;'EOF'\n{\n  \"instances\": [\n    {\n      \"sepal_length\": 5.1,\n      \"sepal_width\": 3.5,\n      \"petal_length\": 1.4,\n      \"petal_width\": 0.2\n    },\n    {\n      \"features\": [6.2, 2.8, 4.8, 1.8]\n    }\n  ]\n}\nEOF\n<\/code><\/pre>\n\n\n\n<p>Call the endpoint with an access token:<\/p>\n\n\n\n<pre><code class=\"language-bash\">TOKEN=\"$(gcloud auth print-access-token)\"\n\nPREDICT_URL=\"https:\/\/${REGION}-aiplatform.googleapis.com\/v1\/${ENDPOINT_ID}:predict\"\necho \"$PREDICT_URL\"\n\ncurl -s \\\n  -H \"Authorization: Bearer ${TOKEN}\" \\\n  -H \"Content-Type: application\/json\" \\\n  \"${PREDICT_URL}\" \\\n  -d @request.json | python -m json.tool\n<\/code><\/pre>\n\n\n\n<p><strong>Expected outcome<\/strong>: A JSON response with a <code>predictions<\/code> list, for example:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><code>class_name<\/code>: <code>setosa<\/code> for the first instance (commonly)<\/li>\n<li>Probability distribution across three classes<\/li>\n<\/ul>\n\n\n\n<p>If you get <code>PERMISSION_DENIED<\/code>, see Troubleshooting.<\/p>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h3 class=\"wp-block-heading\">Step 10: (Optional) Check logs and basic metrics<\/h3>\n\n\n\n<h4 class=\"wp-block-heading\">Cloud Logging<\/h4>\n\n\n\n<p>In the Google Cloud Console:\n&#8211; Go to <strong>Logging \u2192 Logs Explorer<\/strong>\n&#8211; Resource type: search for Vertex AI resources (availability can vary)\n&#8211; Filter by the endpoint ID or by <code>aiplatform.googleapis.com<\/code><\/p>\n\n\n\n<p>If you enabled request\/response logging explicitly (not done in this minimal lab), you may see more payload detail. Even without payload logging, you should see operational logs and audit logs for deployment actions.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Cloud Monitoring<\/h4>\n\n\n\n<p>In the Google Cloud Console:\n&#8211; Go to <strong>Monitoring \u2192 Metrics Explorer<\/strong>\n&#8211; Search for Vertex AI endpoint metrics (names and availability can evolve)<\/p>\n\n\n\n<p><strong>Expected outcome<\/strong>: You can locate endpoint activity, request counts, and latency metrics (exact metric names may vary; verify in official docs).<\/p>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h3 class=\"wp-block-heading\">Validation<\/h3>\n\n\n\n<p>Use this checklist:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>\n<p><strong>Model exists<\/strong>:\n   <code>bash\n   gcloud ai models list --region=\"$REGION\" --filter=\"displayName=$MODEL_DISPLAY_NAME\"<\/code><\/p>\n<\/li>\n<li>\n<p><strong>Endpoint exists<\/strong>:\n   <code>bash\n   gcloud ai endpoints list --region=\"$REGION\" --filter=\"displayName=$ENDPOINT_DISPLAY_NAME\"<\/code><\/p>\n<\/li>\n<li>\n<p><strong>Model is deployed<\/strong>:\n   <code>bash\n   gcloud ai endpoints describe \"$ENDPOINT_ID\" --region=\"$REGION\" --format=\"yaml(deployedModels)\"<\/code><\/p>\n<\/li>\n<li>\n<p><strong>Prediction works<\/strong>: <code>curl<\/code> to <code>:predict<\/code> returns <code>predictions<\/code> with class names.<\/p>\n<\/li>\n<\/ol>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h3 class=\"wp-block-heading\">Troubleshooting<\/h3>\n\n\n\n<p>Common issues and fixes:<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Error: <code>PERMISSION_DENIED<\/code> when calling <code>:predict<\/code><\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Ensure the caller has permission to invoke predictions.<\/li>\n<li>For production, grant a service account the minimum role needed (often a Vertex AI user\/invoker-style role; exact roles and permissions should be verified in IAM docs for Vertex AI).<\/li>\n<li>For testing with your user, ensure your user has Vertex AI permissions in the project.<\/li>\n<\/ul>\n\n\n\n<p>Also confirm you are using the right endpoint URL:\n&#8211; <code>https:\/\/REGION-aiplatform.googleapis.com\/v1\/projects\/PROJECT\/locations\/REGION\/endpoints\/ENDPOINT_ID:predict<\/code><\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Error: container fails health checks \/ deployment fails<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Confirm your container listens on port <code>AIP_HTTP_PORT<\/code> (default 8080).<\/li>\n<li>Confirm <code>--container-health-route=\"\/health\"<\/code> matches your implementation.<\/li>\n<li>Confirm your app starts quickly; long initialization can cause timeouts.<\/li>\n<li>Review Cloud Logging for deployment errors.<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\">Error: <code>RESOURCE_EXHAUSTED<\/code> or quota-related failures<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Check Vertex AI quotas for deployed compute in your region.<\/li>\n<li>Reduce replica counts or use a smaller machine type.<\/li>\n<li>Request quota increases if needed.<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\">Error: <code>NOT_FOUND<\/code> for model or endpoint<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Ensure you are using the same region for all commands.<\/li>\n<li>Vertex AI resources are location-scoped; <code>us-central1<\/code> resources aren\u2019t visible in <code>europe-west4<\/code>.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h3 class=\"wp-block-heading\">Cleanup<\/h3>\n\n\n\n<p>To stop charges, undeploy and delete resources.<\/p>\n\n\n\n<p>1) Undeploy model from endpoint<br\/>\nFind the deployed model ID:<\/p>\n\n\n\n<pre><code class=\"language-bash\">gcloud ai endpoints describe \"$ENDPOINT_ID\" --region=\"$REGION\" --format=\"yaml(deployedModels)\"\n<\/code><\/pre>\n\n\n\n<p>Look for <code>deployedModelId<\/code>. Then:<\/p>\n\n\n\n<pre><code class=\"language-bash\">DEPLOYED_MODEL_ID=\"REPLACE_WITH_DEPLOYED_MODEL_ID\"\n\ngcloud ai endpoints undeploy-model \"$ENDPOINT_ID\" \\\n  --region=\"$REGION\" \\\n  --deployed-model-id=\"$DEPLOYED_MODEL_ID\"\n<\/code><\/pre>\n\n\n\n<p>2) Delete endpoint:<\/p>\n\n\n\n<pre><code class=\"language-bash\">gcloud ai endpoints delete \"$ENDPOINT_ID\" --region=\"$REGION\" --quiet\n<\/code><\/pre>\n\n\n\n<p>3) Delete model:<\/p>\n\n\n\n<pre><code class=\"language-bash\">gcloud ai models delete \"$MODEL_ID\" --region=\"$REGION\" --quiet\n<\/code><\/pre>\n\n\n\n<p>4) Delete Artifact Registry repository (deletes images too):<\/p>\n\n\n\n<pre><code class=\"language-bash\">gcloud artifacts repositories delete \"$REPO\" --location=\"$REGION\" --quiet\n<\/code><\/pre>\n\n\n\n<p>5) Delete Cloud Storage bucket (optional):<\/p>\n\n\n\n<pre><code class=\"language-bash\">gsutil -m rm -r \"$BUCKET\"\n<\/code><\/pre>\n\n\n\n<p><strong>Expected outcome<\/strong>: No deployed replicas remain; ongoing Vertex AI Prediction serving charges stop.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">11. Best Practices<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Architecture best practices<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Keep serving close to callers<\/strong>: Deploy endpoints in the same region as Cloud Run\/GKE services invoking them to reduce latency and egress.<\/li>\n<li><strong>Separate environments<\/strong>: Use separate projects (preferred) or at least separate endpoints\/models for dev\/stage\/prod.<\/li>\n<li><strong>Use traffic splitting for safe releases<\/strong>: Canary new models with small percentages and monitor before full rollout.<\/li>\n<li><strong>Choose online vs batch intentionally<\/strong>:<\/li>\n<li>Online for synchronous UX flows<\/li>\n<li>Batch for large offline scoring, backfills, and nightly jobs<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">IAM\/security best practices<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Least privilege<\/strong>:<\/li>\n<li>Separate roles for model deployers (CI\/CD) and model invokers (apps).<\/li>\n<li>Avoid granting broad <code>aiplatform.admin<\/code> to runtime service accounts.<\/li>\n<li><strong>Use dedicated service accounts<\/strong> per application and environment.<\/li>\n<li><strong>Restrict who can deploy<\/strong> models to production endpoints (deployment is a high-impact permission).<\/li>\n<li><strong>Use VPC Service Controls<\/strong> for sensitive data workloads (verify applicability).<\/li>\n<li><strong>Avoid logging PII<\/strong> in prediction payloads.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Cost best practices<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Min replicas = 1<\/strong> for dev\/test endpoints; undeploy when not needed.<\/li>\n<li>Use autoscaling carefully; set max replicas to control worst-case cost.<\/li>\n<li>Prefer CPU unless latency or model architecture requires GPU.<\/li>\n<li>Use batch prediction for occasional bulk scoring rather than keeping endpoints running.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Performance best practices<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Keep payloads small; do not send raw large objects in prediction payloads.<\/li>\n<li>Preprocess outside the endpoint when possible (e.g., in Cloud Run) to reduce model compute time.<\/li>\n<li>Load models efficiently (avoid slow cold-start logic in containers).<\/li>\n<li>Use appropriate machine types and replicas; test with realistic traffic.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Reliability best practices<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Deploy at least two replicas for high availability (balanced against cost).<\/li>\n<li>Use rollback plans: keep previous model version deployed until new model is proven.<\/li>\n<li>Use timeouts and retries on the client side with backoff (but avoid thundering herds).<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Operations best practices<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Create dashboards and alerts for:<\/li>\n<li>error rate<\/li>\n<li>p95\/p99 latency<\/li>\n<li>request volume<\/li>\n<li>saturation (if available)<\/li>\n<li>Use structured logging in custom containers.<\/li>\n<li>Track model version, data schema version, and feature definitions as part of change management.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Governance\/tagging\/naming best practices<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Consistent naming scheme:<\/li>\n<li><code>env-app-modelname-version<\/code><\/li>\n<li><code>env-app-endpoint<\/code><\/li>\n<li>Apply labels for cost allocation (team, environment, app, owner).<\/li>\n<li>Maintain model metadata: training data snapshot reference, evaluation metrics, approval status.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">12. Security Considerations<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Identity and access model<\/h3>\n\n\n\n<p>Vertex AI Prediction uses Google Cloud IAM:\n&#8211; Administrative actions (create endpoint, deploy model) require privileged roles.\n&#8211; Invocation requires permission to call <code>:predict<\/code> on the endpoint (and sometimes related permissions). Verify exact permissions and roles in official Vertex AI IAM documentation.<\/p>\n\n\n\n<p>Recommended patterns:\n&#8211; Use a <strong>runtime service account<\/strong> for the calling application.\n&#8211; Grant that service account only what it needs to invoke predictions.\n&#8211; Keep deployment privileges restricted to CI\/CD identities.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Encryption<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Data in transit uses HTTPS\/TLS when calling the Vertex AI API.<\/li>\n<li>Data at rest for artifacts stored in Cloud Storage and Artifact Registry is encrypted by default in Google Cloud.<\/li>\n<li>For customer-managed encryption keys (CMEK), verify current Vertex AI support and configuration requirements in official docs (capability can vary by resource type and location).<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Network exposure<\/h3>\n\n\n\n<p>Default prediction endpoints are reachable via Google APIs over the public internet (authenticated). To reduce exposure:\n&#8211; Use private access patterns for Google APIs where feasible.\n&#8211; Consider Private Service Connect and VPC Service Controls (verify current applicability and constraints).\n&#8211; Keep calling services in private subnets and restrict outbound paths.<\/p>\n\n\n\n<p>If you need WAF-like controls or custom auth at the edge, consider fronting the invocation with a controlled proxy service (for example, Cloud Run + API Gateway) that enforces your policies\u2014while still using Vertex AI for inference. This adds complexity but can be justified in regulated environments.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Secrets handling<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Do not bake secrets into containers.<\/li>\n<li>Use <strong>Secret Manager<\/strong> and inject secrets into calling services (Cloud Run\/GKE).<\/li>\n<li>For model endpoints, avoid requiring secrets inside the prediction container; rely on IAM where possible.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Audit\/logging<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Cloud Audit Logs captures administrative actions for Vertex AI resources.<\/li>\n<li>If you enable prediction payload logging, treat it as sensitive:<\/li>\n<li>Avoid logging raw PII<\/li>\n<li>Use sampling<\/li>\n<li>Apply retention controls<\/li>\n<li>Restrict log access via IAM<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Compliance considerations<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Data residency: choose regions aligned with compliance needs.<\/li>\n<li>Retention: manage logs and artifacts retention policies.<\/li>\n<li>Access control: implement least privilege, separation of duties, and approval workflows for deploying models.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Common security mistakes<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Granting broad admin roles to application runtime identities<\/li>\n<li>Logging full request payloads that contain sensitive identifiers<\/li>\n<li>Cross-region calls that inadvertently move regulated data<\/li>\n<li>Leaving dev endpoints deployed 24\/7 with permissive IAM<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Secure deployment recommendations<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Use separate projects for prod vs non-prod.<\/li>\n<li>Use dedicated service accounts and minimal IAM roles.<\/li>\n<li>Combine perimeter controls (VPC-SC), private access patterns, and strict logging policies for sensitive workloads.<\/li>\n<li>Adopt a model promotion workflow (review \u2192 approval \u2192 deployment) rather than ad-hoc deployments.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">13. Limitations and Gotchas<\/h2>\n\n\n\n<p>The exact limits and behavior depend on region, model type, and current platform updates. Validate with official docs and quotas.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Known limitations \/ common constraints<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Regional scoping<\/strong>: Models and endpoints are location-scoped; you must keep resources aligned in the same region.<\/li>\n<li><strong>Always-on cost for online endpoints<\/strong>: Minimum replicas incur ongoing charges.<\/li>\n<li><strong>Cold starts and container startup time<\/strong>: Custom containers that take too long to start can fail health checks.<\/li>\n<li><strong>Payload\/logging risk<\/strong>: Request\/response logging can create privacy and cost issues.<\/li>\n<li><strong>Quota constraints<\/strong>: GPU availability and deployed node quotas can be tight in some regions.<\/li>\n<li><strong>Client-side complexity<\/strong>: If you need user\/session-based routing for A\/B tests, you must implement it in the calling service; endpoint traffic split is percentage-based.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Pricing surprises<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Paying for deployed compute even at zero QPS (online).<\/li>\n<li>Logging ingestion costs when enabling detailed payload logging.<\/li>\n<li>Cross-region network egress when callers are outside the endpoint region.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Compatibility issues<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Custom containers must adhere to Vertex AI\u2019s serving contract (routes, port, request\/response format).<\/li>\n<li>Some advanced features (like integrated explanation or monitoring features) may require specific model formats and configurations.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Migration challenges<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Migrating from legacy AI Platform Prediction may require:<\/li>\n<li>Updating APIs and resource naming (locations, endpoints)<\/li>\n<li>Updating CI\/CD and IAM patterns<\/li>\n<li>Adjusting container contracts and logging behavior<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">14. Comparison with Alternatives<\/h2>\n\n\n\n<p>Vertex AI Prediction is one option in a broader serving landscape.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table>\n<thead>\n<tr>\n<th>Option<\/th>\n<th>Best For<\/th>\n<th>Strengths<\/th>\n<th>Weaknesses<\/th>\n<th>When to Choose<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td><strong>Vertex AI Prediction (Google Cloud)<\/strong><\/td>\n<td>Managed online + batch inference with IAM and MLOps integration<\/td>\n<td>Managed endpoints, traffic splitting, autoscaling, strong GCP integration<\/td>\n<td>Always-on endpoint cost; less control than self-managed<\/td>\n<td>When you want managed serving with governance and predictable ops<\/td>\n<\/tr>\n<tr>\n<td><strong>Cloud Run (Google Cloud)<\/strong><\/td>\n<td>Lightweight inference microservices<\/td>\n<td>Simple deployment, scale-to-zero, easy custom auth<\/td>\n<td>You manage serving logic and scaling characteristics; no native model registry\/endpoint features<\/td>\n<td>When models are small and you want serverless scale-to-zero and full HTTP control<\/td>\n<\/tr>\n<tr>\n<td><strong>GKE + KServe (self-managed on Google Cloud)<\/strong><\/td>\n<td>Highly custom, Kubernetes-native ML serving<\/td>\n<td>Maximum control, flexible networking, advanced patterns<\/td>\n<td>Operational complexity, cluster management, security hardening effort<\/td>\n<td>When you need deep customization and already run mature Kubernetes platform<\/td>\n<\/tr>\n<tr>\n<td><strong>BigQuery ML predictions<\/strong><\/td>\n<td>In-warehouse scoring and SQL workflows<\/td>\n<td>No serving infra; simple batch scoring in SQL<\/td>\n<td>Not a general low-latency serving API<\/td>\n<td>When predictions are primarily analytical\/batch and live in BigQuery workflows<\/td>\n<\/tr>\n<tr>\n<td><strong>Amazon SageMaker real-time endpoints (AWS)<\/strong><\/td>\n<td>AWS-native managed inference<\/td>\n<td>Strong AWS ecosystem integration<\/td>\n<td>Different IAM\/networking model; cross-cloud complexity<\/td>\n<td>When most of your stack is on AWS<\/td>\n<\/tr>\n<tr>\n<td><strong>Azure ML Online Endpoints (Azure)<\/strong><\/td>\n<td>Azure-native managed inference<\/td>\n<td>Azure ecosystem integration<\/td>\n<td>Different governance and ops model<\/td>\n<td>When most of your stack is on Azure<\/td>\n<\/tr>\n<tr>\n<td><strong>Self-managed (BentoML\/FastAPI on VMs)<\/strong><\/td>\n<td>Maximum simplicity or special constraints<\/td>\n<td>Full control, portable<\/td>\n<td>You manage scaling, HA, patching, security<\/td>\n<td>When you need portability or have strict infra constraints<\/td>\n<\/tr>\n<\/tbody>\n<\/table><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">15. Real-World Example<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Enterprise example: regulated financial services risk scoring<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Problem<\/strong>: A bank needs to score transactions in real time for fraud risk, with strict access controls and auditability.<\/li>\n<li><strong>Proposed architecture<\/strong>:<\/li>\n<li>Cloud Run service receives transaction events (or synchronously from an API)<\/li>\n<li>Service performs feature assembly (from BigQuery or low-latency cache)<\/li>\n<li>Cloud Run calls <strong>Vertex AI Endpoint<\/strong> for online prediction<\/li>\n<li>Responses stored in BigQuery and logged (without sensitive payload fields)<\/li>\n<li>VPC Service Controls perimeter applied; private access patterns used for Google APIs where required<\/li>\n<li>CI\/CD pipeline deploys new model versions with 5% canary traffic split<\/li>\n<li><strong>Why Vertex AI Prediction was chosen<\/strong>:<\/li>\n<li>Managed inference with IAM, audit logs, and rollout controls<\/li>\n<li>Reduced operational burden versus self-managed Kubernetes serving<\/li>\n<li><strong>Expected outcomes<\/strong>:<\/li>\n<li>Faster and safer model releases<\/li>\n<li>Improved reliability and visibility into latency\/error rates<\/li>\n<li>Stronger compliance posture via IAM, auditability, and perimeter controls<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Startup\/small-team example: churn prediction for a SaaS product<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Problem<\/strong>: A small team needs churn scores in-app for account managers and a nightly batch list for outreach campaigns.<\/li>\n<li><strong>Proposed architecture<\/strong>:<\/li>\n<li>Vertex AI model trained weekly<\/li>\n<li>One small online endpoint for in-app scoring<\/li>\n<li>BatchPredictionJob runs nightly to score all customers and writes results to BigQuery<\/li>\n<li>Minimal logging and tight cost controls (min replicas = 1, small machine type)<\/li>\n<li><strong>Why Vertex AI Prediction was chosen<\/strong>:<\/li>\n<li>Fast path to production without hiring dedicated infra engineers<\/li>\n<li>Batch and online options with consistent tooling<\/li>\n<li><strong>Expected outcomes<\/strong>:<\/li>\n<li>Account managers get real-time churn signals<\/li>\n<li>Marketing gets batch segments without building data pipelines from scratch<\/li>\n<li>Cost stays predictable with controlled endpoint sizing and scheduled batch jobs<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">16. FAQ<\/h2>\n\n\n\n<p>1) <strong>Is \u201cVertex AI Prediction\u201d a separate product from Vertex AI?<\/strong><br\/>\nVertex AI Prediction is a functional area within Vertex AI focused on online and batch inference. In pricing and documentation, it may appear as a separate category, but it\u2019s part of Vertex AI.<\/p>\n\n\n\n<p>2) <strong>What\u2019s the difference between online prediction and batch prediction?<\/strong><br\/>\nOnline prediction serves real-time requests from an endpoint (low latency). Batch prediction runs offline jobs to score large datasets and write outputs to storage.<\/p>\n\n\n\n<p>3) <strong>Do I pay per request for online prediction?<\/strong><br\/>\nTypically, online prediction cost is dominated by deployed compute time (replica\/node hours) rather than per-request charges. Verify current SKUs on the official pricing page.<\/p>\n\n\n\n<p>4) <strong>Why does my endpoint cost money even when idle?<\/strong><br\/>\nBecause the minimum replica count keeps compute running to serve requests with low latency. For dev\/test, undeploy when not needed.<\/p>\n\n\n\n<p>5) <strong>Can I deploy multiple versions to one endpoint?<\/strong><br\/>\nYes. You can deploy multiple models to a single endpoint and split traffic by percentage for canary\/A\/B rollouts.<\/p>\n\n\n\n<p>6) <strong>Can I call a Vertex AI endpoint from on-premises?<\/strong><br\/>\nYes, as long as you can reach Google APIs endpoints and authenticate with IAM. For private connectivity requirements, evaluate private access patterns and verify official guidance.<\/p>\n\n\n\n<p>7) <strong>How do I secure endpoint invocation?<\/strong><br\/>\nUse IAM with least privilege and call endpoints using a dedicated service account from your application. Restrict who can deploy and manage endpoints.<\/p>\n\n\n\n<p>8) <strong>How do I reduce latency?<\/strong><br\/>\nDeploy in the same region as the caller, keep payloads small, optimize container startup and inference time, and scale replicas appropriately.<\/p>\n\n\n\n<p>9) <strong>What is the biggest operational risk with custom containers?<\/strong><br\/>\nFailing health checks due to slow startup, incorrect routes\/ports, or request format mismatches. Always test containers locally and validate logs.<\/p>\n\n\n\n<p>10) <strong>Can I use GPUs for inference?<\/strong><br\/>\nOften yes, depending on model and configuration. GPU availability is region- and quota-dependent. Verify supported accelerators and SKUs in official docs.<\/p>\n\n\n\n<p>11) <strong>How do I do blue\/green deployment?<\/strong><br\/>\nDeploy the new model alongside the old one and switch traffic split from 0% \u2192 100% after validation. Keep the old model available for rollback.<\/p>\n\n\n\n<p>12) <strong>Can I run batch predictions from BigQuery directly?<\/strong><br\/>\nBatch workflows commonly use Cloud Storage; BigQuery integration exists in various ways across GCP. Verify current supported sources\/sinks for Vertex AI batch prediction in official docs for your model type and region.<\/p>\n\n\n\n<p>13) <strong>What logs are available for predictions?<\/strong><br\/>\nAdministrative actions are in Cloud Audit Logs. Prediction request\/response logging can be enabled with controls (often including sampling). Be cautious with sensitive data.<\/p>\n\n\n\n<p>14) <strong>How do I track which model version produced a prediction?<\/strong><br\/>\nInclude model version identifiers in deployment metadata, and log the deployed model ID (or use separate endpoints). If you log prediction metadata, avoid sensitive payload fields.<\/p>\n\n\n\n<p>15) <strong>Is Vertex AI Prediction suitable for strict compliance environments?<\/strong><br\/>\nIt can be, when combined with correct IAM, logging controls, region selection, and perimeter\/network controls like VPC Service Controls. Always validate your compliance requirements and platform capabilities in official docs.<\/p>\n\n\n\n<p>16) <strong>What is the difference between Vertex AI Prediction and serving on Cloud Run?<\/strong><br\/>\nCloud Run gives you more control and scale-to-zero, but you operate the serving stack and deployment patterns yourself. Vertex AI Prediction provides managed ML-serving constructs like model registry integration and traffic splitting.<\/p>\n\n\n\n<p>17) <strong>How do I stop all costs quickly?<\/strong><br\/>\nUndeploy models from endpoints (or delete endpoints). Deleting the model resource alone does not necessarily stop serving costs if it is still deployed.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">17. Top Online Resources to Learn Vertex AI Prediction<\/h2>\n\n\n\n<figure class=\"wp-block-table\"><table>\n<thead>\n<tr>\n<th>Resource Type<\/th>\n<th>Name<\/th>\n<th>Why It Is Useful<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Official documentation<\/td>\n<td>Vertex AI documentation<\/td>\n<td>Primary source for current features, APIs, concepts: https:\/\/cloud.google.com\/vertex-ai\/docs<\/td>\n<\/tr>\n<tr>\n<td>Official docs (prediction)<\/td>\n<td>Vertex AI: Online prediction overview<\/td>\n<td>Core endpoint concepts and how prediction works (verify current URL path in docs): https:\/\/cloud.google.com\/vertex-ai\/docs\/predictions\/overview<\/td>\n<\/tr>\n<tr>\n<td>Official docs (batch)<\/td>\n<td>Vertex AI: Batch prediction overview<\/td>\n<td>How to run BatchPredictionJob and supported I\/O formats: https:\/\/cloud.google.com\/vertex-ai\/docs\/predictions\/batch-predictions<\/td>\n<\/tr>\n<tr>\n<td>Official API reference<\/td>\n<td>Vertex AI API reference<\/td>\n<td>Resource schemas, methods, and request formats: https:\/\/cloud.google.com\/vertex-ai\/docs\/reference\/rest<\/td>\n<\/tr>\n<tr>\n<td>Official pricing<\/td>\n<td>Vertex AI pricing<\/td>\n<td>Authoritative SKUs and billing units: https:\/\/cloud.google.com\/vertex-ai\/pricing<\/td>\n<\/tr>\n<tr>\n<td>Pricing tool<\/td>\n<td>Google Cloud Pricing Calculator<\/td>\n<td>Region-specific estimates and what-if scenarios: https:\/\/cloud.google.com\/products\/calculator<\/td>\n<\/tr>\n<tr>\n<td>Architecture guidance<\/td>\n<td>Google Cloud Architecture Center<\/td>\n<td>Reference architectures and best practices: https:\/\/cloud.google.com\/architecture<\/td>\n<\/tr>\n<tr>\n<td>Official samples<\/td>\n<td>GoogleCloudPlatform GitHub org<\/td>\n<td>Many Vertex AI examples and samples live here: https:\/\/github.com\/GoogleCloudPlatform<\/td>\n<\/tr>\n<tr>\n<td>Official Vertex AI samples<\/td>\n<td>Vertex AI samples (search within repo\/org)<\/td>\n<td>Practical code for models, endpoints, monitoring; verify current repo paths: https:\/\/github.com\/GoogleCloudPlatform\/vertex-ai-samples<\/td>\n<\/tr>\n<tr>\n<td>Videos<\/td>\n<td>Google Cloud Tech (YouTube)<\/td>\n<td>Product overviews and practical walkthroughs; search \u201cVertex AI prediction\u201d: https:\/\/www.youtube.com\/@GoogleCloudTech<\/td>\n<\/tr>\n<\/tbody>\n<\/table><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">18. Training and Certification Providers<\/h2>\n\n\n\n<p>The following providers may offer training related to Google Cloud, AI and ML, and Vertex AI Prediction. Verify current course offerings directly on their websites.<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>\n<p><strong>DevOpsSchool.com<\/strong>\n   &#8211; Suitable audience: DevOps engineers, SREs, cloud engineers, platform teams, developers\n   &#8211; Likely learning focus: Google Cloud, DevOps, MLOps fundamentals, operationalization patterns\n   &#8211; Mode: check website\n   &#8211; Website URL: https:\/\/www.devopsschool.com\/<\/p>\n<\/li>\n<li>\n<p><strong>ScmGalaxy.com<\/strong>\n   &#8211; Suitable audience: DevOps and automation practitioners, engineering teams\n   &#8211; Likely learning focus: tooling, CI\/CD, automation concepts that can support MLOps\n   &#8211; Mode: check website\n   &#8211; Website URL: https:\/\/www.scmgalaxy.com\/<\/p>\n<\/li>\n<li>\n<p><strong>CLoudOpsNow.in<\/strong>\n   &#8211; Suitable audience: Cloud operations and platform teams\n   &#8211; Likely learning focus: cloud operations, deployment patterns, reliability practices (verify Vertex AI coverage)\n   &#8211; Mode: check website\n   &#8211; Website URL: https:\/\/cloudopsnow.in\/<\/p>\n<\/li>\n<li>\n<p><strong>SreSchool.com<\/strong>\n   &#8211; Suitable audience: SREs, operations teams, reliability-focused engineers\n   &#8211; Likely learning focus: SRE principles, monitoring\/alerting, reliability practices applicable to ML serving\n   &#8211; Mode: check website\n   &#8211; Website URL: https:\/\/sreschool.com\/<\/p>\n<\/li>\n<li>\n<p><strong>AiOpsSchool.com<\/strong>\n   &#8211; Suitable audience: AIOps practitioners, operations and data teams\n   &#8211; Likely learning focus: operations automation, monitoring\/analytics concepts, AI in ops contexts\n   &#8211; Mode: check website\n   &#8211; Website URL: https:\/\/aiopsschool.com\/<\/p>\n<\/li>\n<\/ol>\n\n\n\n<h2 class=\"wp-block-heading\">19. Top Trainers<\/h2>\n\n\n\n<p>These sites may provide trainer directories, training services, or related resources. Verify background, course scope, and credentials on each site.<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>\n<p><strong>RajeshKumar.xyz<\/strong>\n   &#8211; Likely specialization: DevOps\/cloud training and guidance (verify current offerings)\n   &#8211; Suitable audience: engineers seeking practical guidance and training resources\n   &#8211; Website URL: https:\/\/rajeshkumar.xyz\/<\/p>\n<\/li>\n<li>\n<p><strong>devopstrainer.in<\/strong>\n   &#8211; Likely specialization: DevOps training and coaching (verify Google Cloud\/MLOps coverage)\n   &#8211; Suitable audience: DevOps engineers, cloud engineers, students\n   &#8211; Website URL: https:\/\/devopstrainer.in\/<\/p>\n<\/li>\n<li>\n<p><strong>devopsfreelancer.com<\/strong>\n   &#8211; Likely specialization: freelance DevOps services and training resources (verify current scope)\n   &#8211; Suitable audience: teams needing short-term expertise or training support\n   &#8211; Website URL: https:\/\/devopsfreelancer.com\/<\/p>\n<\/li>\n<li>\n<p><strong>devopssupport.in<\/strong>\n   &#8211; Likely specialization: DevOps support services and learning resources (verify current scope)\n   &#8211; Suitable audience: operations teams, engineers needing hands-on support\n   &#8211; Website URL: https:\/\/devopssupport.in\/<\/p>\n<\/li>\n<\/ol>\n\n\n\n<h2 class=\"wp-block-heading\">20. Top Consulting Companies<\/h2>\n\n\n\n<p>These organizations may provide consulting services related to cloud, DevOps, and engineering practices that can support Vertex AI Prediction adoption. Verify specific Vertex AI capabilities and references directly with each company.<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>\n<p><strong>cotocus.com<\/strong>\n   &#8211; Likely service area: cloud consulting, DevOps, platform engineering (verify current portfolio)\n   &#8211; Where they may help: architecture, delivery planning, platform setup, operational readiness\n   &#8211; Consulting use case examples:<\/p>\n<ul>\n<li>Designing a secure inference architecture on Google Cloud<\/li>\n<li>Implementing CI\/CD for model deployments<\/li>\n<li>Setting up monitoring\/alerts for endpoints<\/li>\n<li>Website URL: https:\/\/cotocus.com\/<\/li>\n<\/ul>\n<\/li>\n<li>\n<p><strong>DevOpsSchool.com<\/strong>\n   &#8211; Likely service area: DevOps consulting, training, platform enablement (verify consulting offerings)\n   &#8211; Where they may help: skills enablement + implementation support for cloud\/DevOps practices\n   &#8211; Consulting use case examples:<\/p>\n<ul>\n<li>Building an MLOps workflow integrating Vertex AI Prediction<\/li>\n<li>Standardizing IAM and deployment pipelines across environments<\/li>\n<li>Cost optimization and operational playbooks for serving<\/li>\n<li>Website URL: https:\/\/www.devopsschool.com\/<\/li>\n<\/ul>\n<\/li>\n<li>\n<p><strong>DEVOPSCONSULTING.IN<\/strong>\n   &#8211; Likely service area: DevOps and cloud consulting (verify current services)\n   &#8211; Where they may help: delivery acceleration, operational tooling, reliability practices\n   &#8211; Consulting use case examples:<\/p>\n<ul>\n<li>Setting up release strategies (canary\/blue-green) for ML endpoints<\/li>\n<li>Integrating prediction endpoints with Cloud Run\/GKE applications<\/li>\n<li>Establishing governance controls (naming, tagging, audit)<\/li>\n<li>Website URL: https:\/\/devopsconsulting.in\/<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n\n\n\n<h2 class=\"wp-block-heading\">21. Career and Learning Roadmap<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">What to learn before Vertex AI Prediction<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Google Cloud fundamentals:<\/li>\n<li>Projects, billing, IAM, service accounts<\/li>\n<li>Networking basics (regions, VPC, Private Google Access concepts)<\/li>\n<li>Container fundamentals:<\/li>\n<li>Dockerfile basics<\/li>\n<li>Artifact Registry usage<\/li>\n<li>ML fundamentals (practical, not theoretical-heavy):<\/li>\n<li>Feature engineering basics<\/li>\n<li>Model evaluation metrics<\/li>\n<li>Overfitting and validation<\/li>\n<li>API basics:<\/li>\n<li>REST\/JSON<\/li>\n<li>Authentication using OAuth2 access tokens<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">What to learn after Vertex AI Prediction<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>MLOps and lifecycle management:<\/li>\n<li>Automated training pipelines (Vertex AI Pipelines)<\/li>\n<li>Model evaluation and governance<\/li>\n<li>Data validation and drift monitoring patterns<\/li>\n<li>Observability:<\/li>\n<li>SLOs for prediction services (latency, availability, error rate)<\/li>\n<li>Structured logging and trace correlation patterns<\/li>\n<li>Security hardening:<\/li>\n<li>VPC Service Controls design<\/li>\n<li>Org Policies, least-privilege IAM, separation of duties<\/li>\n<li>Cost engineering:<\/li>\n<li>Autoscaling tuning and load testing<\/li>\n<li>Batch vs online cost tradeoffs<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Job roles that use it<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>ML Engineer \/ MLOps Engineer<\/li>\n<li>Cloud \/ Platform Engineer supporting ML platforms<\/li>\n<li>DevOps Engineer \/ SRE supporting production inference services<\/li>\n<li>Data Scientist moving models to production (with platform support)<\/li>\n<li>Security Engineer reviewing ML serving architectures<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Certification path (Google Cloud)<\/h3>\n\n\n\n<p>Google Cloud certifications change over time. A common path for teams working with Vertex AI includes:\n&#8211; Associate Cloud Engineer (foundational operations)\n&#8211; Professional Cloud Architect (architecture and governance)\n&#8211; Professional Machine Learning Engineer (ML systems and production ML)<\/p>\n\n\n\n<p>Verify current certification names and exam guides in official Google Cloud certification pages.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Project ideas for practice<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Build a multi-model endpoint with traffic splitting and automated rollback criteria.<\/li>\n<li>Create a batch scoring pipeline (Cloud Storage input \u2192 BatchPredictionJob \u2192 BigQuery output).<\/li>\n<li>Implement a Cloud Run service that:<\/li>\n<li>validates inputs<\/li>\n<li>calls Vertex AI Prediction<\/li>\n<li>logs response metadata safely (no sensitive payloads)<\/li>\n<li>Load test an endpoint and tune autoscaling and machine types for latency\/cost.<\/li>\n<li>Implement a secure invocation design using dedicated service accounts and restricted IAM.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">22. Glossary<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Endpoint (Vertex AI)<\/strong>: A regional resource that hosts one or more deployed models for online prediction.<\/li>\n<li><strong>Model (Vertex AI)<\/strong>: A registered model resource containing metadata and references to artifacts or serving containers.<\/li>\n<li><strong>DeployedModel<\/strong>: A model deployment configuration on an endpoint, including machine type, replicas, and traffic allocation.<\/li>\n<li><strong>Online prediction<\/strong>: Synchronous request\/response inference served by an endpoint.<\/li>\n<li><strong>Batch prediction<\/strong>: Asynchronous offline scoring over many instances via a batch job.<\/li>\n<li><strong>Traffic splitting<\/strong>: Routing a percentage of prediction requests to different deployed models on the same endpoint.<\/li>\n<li><strong>Replica<\/strong>: A running instance of your serving container (or managed serving runtime) handling prediction requests.<\/li>\n<li><strong>Autoscaling<\/strong>: Automatic adjustment of replicas within min\/max bounds based on load.<\/li>\n<li><strong>Artifact Registry<\/strong>: Google Cloud service to store container images and other artifacts.<\/li>\n<li><strong>Cloud Build<\/strong>: Google Cloud CI service used here to build and push container images.<\/li>\n<li><strong>IAM<\/strong>: Identity and Access Management; controls who can manage endpoints\/models and who can invoke predictions.<\/li>\n<li><strong>VPC Service Controls (VPC-SC)<\/strong>: A Google Cloud security feature for defining service perimeters to reduce data exfiltration risks.<\/li>\n<li><strong>Private Service Connect (PSC)<\/strong>: A Google Cloud capability for private connectivity to services; applicability depends on service and configuration.<\/li>\n<li><strong>Cloud Audit Logs<\/strong>: Logs capturing administrative and access events for governance and compliance.<\/li>\n<li><strong>Model monitoring<\/strong>: Observability patterns and (where supported) managed capabilities for detecting drift\/skew and data quality issues.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">23. Summary<\/h2>\n\n\n\n<p>Vertex AI Prediction is the Google Cloud serving layer for <strong>online<\/strong> and <strong>batch<\/strong> model inference in the AI and ML stack. It matters because it provides a managed path to production: endpoints, deployments, rollouts, autoscaling, IAM security, and integration with Google Cloud observability and governance.<\/p>\n\n\n\n<p>From a cost perspective, the key point is that online endpoints typically incur cost based on <strong>deployed compute uptime<\/strong> (minimum replicas), while batch prediction costs track <strong>job compute time<\/strong> plus storage and data processing. From a security perspective, focus on <strong>least-privilege IAM<\/strong>, careful <strong>logging practices<\/strong>, and (when needed) perimeter and private access controls such as <strong>VPC Service Controls<\/strong> and private connectivity patterns.<\/p>\n\n\n\n<p>Use Vertex AI Prediction when you want a managed, production-ready inference platform with rollout controls and strong Google Cloud integration. If you need scale-to-zero HTTP microservices with full control, consider Cloud Run; if you need maximum customization and can operate Kubernetes, consider GKE with KServe.<\/p>\n\n\n\n<p>Next learning step: practice a production rollout pattern\u2014deploy two model versions to one endpoint, split traffic, monitor latency\/error rate, and implement a rollback plan based on objective signals.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>AI and ML<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[53,51],"tags":[],"class_list":["post-572","post","type-post","status-publish","format-standard","hentry","category-ai-and-ml","category-google-cloud"],"_links":{"self":[{"href":"https:\/\/www.devopsschool.com\/tutorials\/wp-json\/wp\/v2\/posts\/572","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.devopsschool.com\/tutorials\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.devopsschool.com\/tutorials\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.devopsschool.com\/tutorials\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.devopsschool.com\/tutorials\/wp-json\/wp\/v2\/comments?post=572"}],"version-history":[{"count":0,"href":"https:\/\/www.devopsschool.com\/tutorials\/wp-json\/wp\/v2\/posts\/572\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.devopsschool.com\/tutorials\/wp-json\/wp\/v2\/media?parent=572"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.devopsschool.com\/tutorials\/wp-json\/wp\/v2\/categories?post=572"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.devopsschool.com\/tutorials\/wp-json\/wp\/v2\/tags?post=572"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}