{"id":566,"date":"2026-04-14T13:11:26","date_gmt":"2026-04-14T13:11:26","guid":{"rendered":"https:\/\/www.devopsschool.com\/tutorials\/google-cloud-vertex-ai-experiments-tutorial-architecture-pricing-use-cases-and-hands-on-guide-for-ai-and-ml\/"},"modified":"2026-04-14T13:11:26","modified_gmt":"2026-04-14T13:11:26","slug":"google-cloud-vertex-ai-experiments-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-experiments-tutorial-architecture-pricing-use-cases-and-hands-on-guide-for-ai-and-ml\/","title":{"rendered":"Google Cloud Vertex AI Experiments 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<h3 class=\"wp-block-heading\">What this service is<\/h3>\n\n\n\n<p>Vertex AI Experiments is Google Cloud\u2019s experiment tracking capability inside Vertex AI. It helps you record, organize, compare, and reproduce machine learning (ML) experiments by tracking runs, parameters (hyperparameters and settings), metrics (accuracy, loss, AUC, etc.), and artifacts (links to models, datasets, and outputs).<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">One-paragraph simple explanation<\/h3>\n\n\n\n<p>When you train models, you quickly end up with lots of \u201cruns\u201d that differ slightly\u2014different learning rates, features, data splits, or model types. Vertex AI Experiments gives you a structured way to log those differences and compare outcomes so you can answer: <em>What changed? Which run is best? Can I reproduce it?<\/em><\/p>\n\n\n\n<h3 class=\"wp-block-heading\">One-paragraph technical explanation<\/h3>\n\n\n\n<p>Technically, Vertex AI Experiments is implemented through Vertex AI\u2019s metadata\/lineage tracking foundations (Vertex AI Metadata) and is integrated into the Vertex AI SDK and Vertex AI Console. You create an <strong>Experiment<\/strong>, start <strong>Runs<\/strong>, log <strong>parameters<\/strong> and <strong>metrics<\/strong>, and optionally link to artifacts such as model resources in Vertex AI Model Registry, pipeline runs in Vertex AI Pipelines, and files in Cloud Storage. This enables consistent experiment lineage across training jobs, notebooks, pipelines, and CI\/CD automation.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">What problem it solves<\/h3>\n\n\n\n<p>Without structured experiment tracking, teams lose time and introduce risk:\n&#8211; Results are scattered across notebooks, logs, and spreadsheets.\n&#8211; Reproducing \u201cthe best\u201d model becomes guesswork.\n&#8211; Model governance and auditability suffer because there\u2019s no clear lineage.\nVertex AI Experiments solves this by providing a centralized, queryable record of experimentation, improving collaboration, reproducibility, and decision-making.<\/p>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">2. What is Vertex AI Experiments?<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Official purpose<\/h3>\n\n\n\n<p>Vertex AI Experiments is designed to <strong>track and compare ML experimentation<\/strong> by capturing run metadata: parameters, metrics, and related artifacts\u2014making it easier to choose, reproduce, and operationalize the best model candidates.<\/p>\n\n\n\n<p>Primary official entry point (verify the latest structure in docs):\n&#8211; https:\/\/cloud.google.com\/vertex-ai\/docs\/experiments\/intro<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Core capabilities<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Create and manage <strong>Experiments<\/strong> (a logical container for work on a problem).<\/li>\n<li>Create and manage <strong>Runs<\/strong> (individual trials\/attempts with metrics and parameters).<\/li>\n<li>Log <strong>parameters<\/strong> (e.g., learning_rate=0.01, model_type=&#8221;xgboost&#8221;).<\/li>\n<li>Log <strong>metrics<\/strong> (e.g., accuracy=0.93, auc=0.98) over time.<\/li>\n<li>View and compare runs in the <strong>Vertex AI Console<\/strong>.<\/li>\n<li>Integrate experiment tracking into:<\/li>\n<li>Vertex AI Workbench notebooks<\/li>\n<li>Custom Python training scripts (local, on VM, or in managed training)<\/li>\n<li>Vertex AI Pipelines<\/li>\n<li>CI\/CD workflows<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Major components<\/h3>\n\n\n\n<p>While \u201cVertex AI Experiments\u201d is the user-facing feature name, it typically involves these conceptual components:<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table>\n<thead>\n<tr>\n<th>Component<\/th>\n<th>What it represents<\/th>\n<th>Where you interact with it<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Experiment<\/td>\n<td>A named container grouping related runs<\/td>\n<td>Vertex AI Console, Vertex AI SDK<\/td>\n<\/tr>\n<tr>\n<td>Run<\/td>\n<td>A single trial with logged metadata<\/td>\n<td>Vertex AI SDK, Console<\/td>\n<\/tr>\n<tr>\n<td>Parameters<\/td>\n<td>Input settings\/hyperparameters\/config<\/td>\n<td>Vertex AI SDK<\/td>\n<\/tr>\n<tr>\n<td>Metrics<\/td>\n<td>Output measurements (final or time-series)<\/td>\n<td>Vertex AI SDK, Console<\/td>\n<\/tr>\n<tr>\n<td>Artifacts\/lineage (related)<\/td>\n<td>Links to models, datasets, pipeline runs, files<\/td>\n<td>Console + other Vertex AI services<\/td>\n<\/tr>\n<\/tbody>\n<\/table><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\">Service type<\/h3>\n\n\n\n<p>Vertex AI Experiments is a <strong>managed experiment tracking capability<\/strong> within the broader <strong>Vertex AI<\/strong> platform (Google Cloud, AI and ML category). It is not typically treated as a stand-alone \u201ccompute service\u201d; it records metadata that your workloads produce.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Scope (regional \/ project-scoped)<\/h3>\n\n\n\n<p>Vertex AI resources are generally <strong>project-scoped<\/strong> and <strong>regional<\/strong> (you choose a Vertex AI location such as <code>us-central1<\/code>). Experiments and runs follow the same pattern: they are created in a project and associated with a Vertex AI region.<\/p>\n\n\n\n<p>Because exact scoping and resource model details can evolve, <strong>verify in official docs<\/strong> for the latest behavior, especially if you operate multiple regions or want centralized governance:\n&#8211; https:\/\/cloud.google.com\/vertex-ai\/docs\/general\/locations<\/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 Experiments fits into a typical Google Cloud ML lifecycle like this:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Data<\/strong>: BigQuery, Cloud Storage, Dataproc, Dataflow<\/li>\n<li><strong>Development<\/strong>: Vertex AI Workbench (notebooks), local dev, Cloud Shell<\/li>\n<li><strong>Training<\/strong>: Vertex AI Training (custom jobs), AutoML (where applicable), pipelines<\/li>\n<li><strong>Tracking &amp; governance<\/strong>: Vertex AI Experiments + Vertex AI Metadata (lineage)<\/li>\n<li><strong>Model management<\/strong>: Vertex AI Model Registry<\/li>\n<li><strong>Serving<\/strong>: Vertex AI endpoints, batch prediction<\/li>\n<li><strong>Observability<\/strong>: Cloud Logging, Cloud Monitoring, Model Monitoring (where applicable)<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">3. Why use Vertex AI Experiments?<\/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 iteration and better decisions<\/strong>: Compare runs and converge on best candidates sooner.<\/li>\n<li><strong>Reduced rework<\/strong>: Avoid retraining \u201cbecause we lost the settings.\u201d<\/li>\n<li><strong>Better collaboration<\/strong>: Teams share a consistent record of experiments across notebooks and scripts.<\/li>\n<li><strong>Audit readiness<\/strong>: A more traceable path from data + code + parameters to chosen model.<\/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>Structured metadata<\/strong>: Standard way to capture parameters and metrics across runs.<\/li>\n<li><strong>Integrates with Vertex AI ecosystem<\/strong>: Easier to connect experiments with pipelines, models, and training jobs than stitching together external tooling.<\/li>\n<li><strong>Reproducibility<\/strong>: Helps enforce consistent logging of dataset versions, git commit hashes, container image tags, and configuration.<\/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>Centralized visibility<\/strong>: Compare runs in one place (console\/SDK), rather than searching logs across machines.<\/li>\n<li><strong>Standardization<\/strong>: Platform teams can provide templates and enforce required metadata fields.<\/li>\n<li><strong>Automation-friendly<\/strong>: Runs can be logged from CI pipelines or scheduled training.<\/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>Google Cloud IAM<\/strong> access control and Cloud Audit Logs integration.<\/li>\n<li><strong>Project-level governance<\/strong>: Aligns with enterprise policies, VPC controls, CMEK (where applicable across dependent services), and logging retention.<\/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><strong>Scales with your workflow<\/strong>: Experiments can track many runs without requiring you to host an experiment tracking server.<\/li>\n<li><strong>Works for distributed teams<\/strong>: Runs can be logged from multiple environments with consistent identity and permission management.<\/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 Experiments when:\n&#8211; You already build or plan to build on <strong>Google Cloud Vertex AI<\/strong>.\n&#8211; You need a <strong>managed<\/strong> experiment tracking experience tied to Google Cloud IAM and auditing.\n&#8211; You want to connect experiment tracking with <strong>Vertex AI Pipelines<\/strong> and the <strong>Model Registry<\/strong>.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">When they should not choose it<\/h3>\n\n\n\n<p>Consider alternatives when:\n&#8211; You are <strong>multi-cloud<\/strong> and need a cloud-agnostic experiment tracking system across providers.\n&#8211; You require specific advanced features found in dedicated third-party platforms (for example, highly customized dashboards, advanced artifact versioning, or deep integrations across non-Google stacks).\n&#8211; Your organization already standardized on a tool like MLflow\/W&amp;B and has mature processes around it (though hybrid approaches are possible).<\/p>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">4. Where is Vertex AI Experiments used?<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Industries<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Finance (risk models, fraud detection)<\/li>\n<li>Healthcare and life sciences (classification, prediction, NLP; compliance-driven auditability)<\/li>\n<li>Retail\/e-commerce (recommendations, demand forecasting)<\/li>\n<li>Manufacturing (predictive maintenance, anomaly detection)<\/li>\n<li>Media\/advertising (CTR prediction, ranking models)<\/li>\n<li>SaaS and tech (NLP, personalization, time-series forecasting)<\/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>Data science teams running frequent model iterations<\/li>\n<li>ML engineering teams operationalizing training into pipelines<\/li>\n<li>Platform engineering teams building \u201cML platforms\u201d on Google Cloud<\/li>\n<li>DevOps\/SRE teams supporting CI\/CD for ML workloads (MLOps)<\/li>\n<li>Governance and risk teams needing traceability and audit logs<\/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>Hyperparameter tuning and model selection<\/li>\n<li>Feature engineering experiments<\/li>\n<li>Architecture comparisons (e.g., XGBoost vs. DNN)<\/li>\n<li>Data preprocessing parameter sweeps<\/li>\n<li>Fine-tuning and evaluation workflows (verify model type support in your workflow)<\/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>Notebook-centric experimentation (Vertex AI Workbench)<\/li>\n<li>Script-based experimentation (local, VM, or containerized)<\/li>\n<li>Pipeline-based experimentation (Vertex AI Pipelines)<\/li>\n<li>CI-driven experimentation (Cloud Build \/ GitHub Actions calling Vertex AI SDK)<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Real-world deployment contexts<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Centralized ML platform in a shared Google Cloud org with multiple teams\/projects<\/li>\n<li>Regulated environments requiring IAM controls and auditing<\/li>\n<li>Startups needing quick iteration with minimal platform overhead<\/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>: Track quick experiments from notebooks or Cloud Shell; validate new features and model types.<\/li>\n<li><strong>Production<\/strong>: Track pipeline runs, training jobs, and evaluation runs; enforce metadata standards; link best runs to models promoted to registry and endpoints.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator\" \/>\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 Experiments fits well.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">1) Hyperparameter exploration for a tabular classifier<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Problem<\/strong>: You need to find the best learning rate, depth, and regularization settings.<\/li>\n<li><strong>Why this service fits<\/strong>: Track each trial as a run with parameters and evaluation metrics.<\/li>\n<li><strong>Example<\/strong>: Run 50 training jobs with different <code>max_depth<\/code> and <code>learning_rate<\/code>; compare AUC and latency metrics in Vertex AI Experiments.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">2) Comparing feature sets for a forecasting model<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Problem<\/strong>: You\u2019re unsure which features improve accuracy without overfitting.<\/li>\n<li><strong>Why this service fits<\/strong>: Log feature set version\/IDs as parameters and compare validation metrics.<\/li>\n<li><strong>Example<\/strong>: Run A uses \u201cbaseline features\u201d; Run B adds promotions; Run C adds weather. Compare MAPE and RMSE.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">3) Reproducible notebook experiments for a team<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Problem<\/strong>: Different analysts run notebooks and results aren\u2019t consistent.<\/li>\n<li><strong>Why this service fits<\/strong>: Standardize logging fields (dataset version, split seed, git commit) across notebook runs.<\/li>\n<li><strong>Example<\/strong>: Each notebook run logs <code>data_snapshot_date<\/code>, <code>seed<\/code>, <code>commit_sha<\/code> so results can be reproduced.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">4) CI-driven model evaluation on every pull request<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Problem<\/strong>: You want automated evaluation that blocks regressions.<\/li>\n<li><strong>Why this service fits<\/strong>: Each CI job logs a run with metrics and pass\/fail thresholds.<\/li>\n<li><strong>Example<\/strong>: Cloud Build triggers evaluation; logs <code>f1_score<\/code>, <code>precision<\/code>, <code>recall<\/code>; PR merges only if <code>f1_score &gt;= baseline - 0.01<\/code>.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">5) Tracking pipeline experiments for end-to-end ML workflows<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Problem<\/strong>: Pipeline changes make it hard to tell what caused metric changes.<\/li>\n<li><strong>Why this service fits<\/strong>: Track each pipeline execution as a run (or link runs) with pipeline parameters and outputs.<\/li>\n<li><strong>Example<\/strong>: Pipeline Run 101 uses new data cleaning step; metrics improve; experiments record pipeline parameter diff.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">6) A\/B testing candidate models before promotion<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Problem<\/strong>: Multiple candidate models meet offline metrics; you must decide what to deploy.<\/li>\n<li><strong>Why this service fits<\/strong>: Use experiments to keep an authoritative record of offline results and model metadata.<\/li>\n<li><strong>Example<\/strong>: Candidate models logged with <code>training_data_version<\/code> and <code>calibration_method<\/code>; best candidate promoted to Model Registry.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">7) Tracking fine-tuning experiments for text classification<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Problem<\/strong>: You try different batch sizes, learning rates, and number of epochs.<\/li>\n<li><strong>Why this service fits<\/strong>: Keep run-by-run metrics and parameters for comparison and reproducibility.<\/li>\n<li><strong>Example<\/strong>: Runs compare <code>epochs=2,3,4<\/code>; track validation F1 and training time.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">8) Regression testing after library or container updates<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Problem<\/strong>: Upgrading TensorFlow\/PyTorch or base images changes results.<\/li>\n<li><strong>Why this service fits<\/strong>: Record environment and dependency versions per run.<\/li>\n<li><strong>Example<\/strong>: Run A uses <code>torch==2.1<\/code>; Run B uses <code>torch==2.2<\/code>; compare metrics and training stability.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">9) Cost\/performance benchmarking<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Problem<\/strong>: You want the best accuracy per dollar and time-to-train.<\/li>\n<li><strong>Why this service fits<\/strong>: Log both model metrics and resource\/cost proxies (training time, machine type).<\/li>\n<li><strong>Example<\/strong>: Compare <code>n1-standard-8<\/code> vs <code>a2-highgpu-1g<\/code> by <code>training_seconds<\/code>, <code>accuracy<\/code>, and <code>cost_estimate_tag<\/code>.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">10) Governance-focused lineage for regulated workloads<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Problem<\/strong>: You need traceability from dataset \u2192 training \u2192 evaluation \u2192 approved model.<\/li>\n<li><strong>Why this service fits<\/strong>: Experiments provide a structured record; can be paired with Model Registry and audit logs.<\/li>\n<li><strong>Example<\/strong>: A \u201ccredit-risk-2026q1\u201d experiment includes runs that link to dataset snapshots and model versions.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">6. Core Features<\/h2>\n\n\n\n<blockquote>\n<p>Note: Feature availability can evolve across regions and SDK versions. Always verify the latest capabilities in official documentation: https:\/\/cloud.google.com\/vertex-ai\/docs\/experiments\/intro<\/p>\n<\/blockquote>\n\n\n\n<h3 class=\"wp-block-heading\">Feature 1: Experiments as logical containers<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>What it does<\/strong>: Lets you group related runs under one experiment name.<\/li>\n<li><strong>Why it matters<\/strong>: Prevents confusion and keeps a clean boundary between projects (e.g., \u201cchurn-model-v3\u201d vs \u201cfraud-detection-baseline\u201d).<\/li>\n<li><strong>Practical benefit<\/strong>: Consistent organization and searchability in Console and via SDK.<\/li>\n<li><strong>Limitations\/caveats<\/strong>: Naming conventions matter; plan for multi-team usage to avoid clutter.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Feature 2: Runs for trial-level tracking<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>What it does<\/strong>: Each run captures a distinct set of parameters, metrics, and metadata.<\/li>\n<li><strong>Why it matters<\/strong>: Real ML iteration happens at run level; the run is your unit of comparison.<\/li>\n<li><strong>Practical benefit<\/strong>: Compare outcomes quickly and see which inputs produced the best results.<\/li>\n<li><strong>Limitations\/caveats<\/strong>: Very high run volume may require governance and conventions; verify quotas\/limits in your project.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Feature 3: Parameter logging<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>What it does<\/strong>: Log key-value pairs that represent inputs to a run (hyperparameters, dataset version, model architecture).<\/li>\n<li><strong>Why it matters<\/strong>: Enables reproducibility and explainability of differences.<\/li>\n<li><strong>Practical benefit<\/strong>: You can answer \u201cwhat changed?\u201d without digging through code or notebooks.<\/li>\n<li><strong>Limitations\/caveats<\/strong>: Teams must standardize parameter names\/types; inconsistent naming reduces value.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Feature 4: Metric logging (including time series)<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>What it does<\/strong>: Log evaluation metrics and (often) intermediate metrics over training steps\/epochs.<\/li>\n<li><strong>Why it matters<\/strong>: ML selection decisions depend on consistent metrics.<\/li>\n<li><strong>Practical benefit<\/strong>: Compare best validation scores, convergence behavior, and stability.<\/li>\n<li><strong>Limitations\/caveats<\/strong>: Ensure metric definitions are consistent (e.g., same validation set); otherwise comparisons can mislead.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Feature 5: Console-based comparison and visualization<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>What it does<\/strong>: View experiments and compare runs in the Vertex AI Console.<\/li>\n<li><strong>Why it matters<\/strong>: Non-developers and stakeholders can review outcomes without running code.<\/li>\n<li><strong>Practical benefit<\/strong>: Quick filtering\/sorting by metrics and parameters.<\/li>\n<li><strong>Limitations\/caveats<\/strong>: UI capabilities evolve; for complex analysis you may still export\/query elsewhere.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Feature 6: SDK integration (Python)<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>What it does<\/strong>: Provides programmatic APIs to create experiments and log data from Python workflows.<\/li>\n<li><strong>Why it matters<\/strong>: Most ML work is scripted; SDK makes tracking easy to standardize.<\/li>\n<li><strong>Practical benefit<\/strong>: Add a few lines to training\/eval scripts to log everything needed.<\/li>\n<li><strong>Limitations\/caveats<\/strong>: SDK versions matter; pin versions and test; review release notes as needed.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Feature 7: Integrations with Vertex AI Pipelines and training jobs (workflow-level)<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>What it does<\/strong>: Enables experiment tracking alongside managed training\/pipelines so runs correspond to executions.<\/li>\n<li><strong>Why it matters<\/strong>: Production ML is often pipelines; tracking must work beyond notebooks.<\/li>\n<li><strong>Practical benefit<\/strong>: Tie pipeline parameters and outputs to run metadata.<\/li>\n<li><strong>Limitations\/caveats<\/strong>: Exact linkage patterns depend on how you structure pipelines; verify best practices in official samples.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Feature 8: Alignment with Vertex AI governance primitives (Metadata\/lineage)<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>What it does<\/strong>: Experiment tracking fits into Vertex AI\u2019s metadata and lineage approach.<\/li>\n<li><strong>Why it matters<\/strong>: Helps build auditable ML systems.<\/li>\n<li><strong>Practical benefit<\/strong>: Easier to connect \u201cwhich data\/code created this model?\u201d<\/li>\n<li><strong>Limitations\/caveats<\/strong>: Full lineage often requires disciplined logging and consistent resource usage across services.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator\" \/>\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 architecture<\/h3>\n\n\n\n<p>At a high level, an ML workload (notebook, training script, pipeline component, or CI job) authenticates to Google Cloud, then uses the Vertex AI SDK to create experiments and runs and log parameters\/metrics. This metadata is stored in Vertex AI\u2019s managed backends and is visible in the Vertex AI Console.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Request\/data\/control flow<\/h3>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>Authentication<\/strong>: Your environment obtains Google Cloud credentials (user ADC in dev; service account in prod).<\/li>\n<li><strong>Initialization<\/strong>: Your code sets the Vertex AI project and location.<\/li>\n<li><strong>Experiment setup<\/strong>: Create\/select an experiment.<\/li>\n<li><strong>Run lifecycle<\/strong>: Start a run \u2192 log parameters \u2192 log metrics \u2192 end run.<\/li>\n<li><strong>Review<\/strong>: View\/compare in Console or query using SDK.<\/li>\n<\/ol>\n\n\n\n<h3 class=\"wp-block-heading\">Integrations with related services<\/h3>\n\n\n\n<p>Common integrations in Google Cloud ML stacks:\n&#8211; <strong>Vertex AI Workbench<\/strong>: run notebooks and log experiments directly.\n&#8211; <strong>Vertex AI Training<\/strong>: training jobs produce metrics; you can log key metrics to Experiments.\n&#8211; <strong>Vertex AI Pipelines<\/strong>: pipeline components can log run metadata; pipelines can parameterize experiments.\n&#8211; <strong>Vertex AI Model Registry<\/strong>: store and version models; you can record model resource names in run parameters\/metadata.\n&#8211; <strong>Cloud Storage<\/strong>: store datasets, models, evaluation outputs; log GCS URIs as parameters\/artifacts.\n&#8211; <strong>BigQuery<\/strong>: store features\/training data; log table snapshot IDs as parameters.\n&#8211; <strong>Cloud Logging\/Monitoring<\/strong>: observe job execution and audit activity.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Dependency services<\/h3>\n\n\n\n<p>Vertex AI Experiments typically depends on:\n&#8211; <strong>Vertex AI API<\/strong> being enabled in the project\n&#8211; IAM permissions to create\/write experiment metadata\n&#8211; (Optional) Cloud Storage for artifacts and outputs\n&#8211; (Optional) Vertex AI TensorBoard for deep training visualization (verify your use case and costs)<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Security\/authentication model<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Uses <strong>Google Cloud IAM<\/strong> for authorization.<\/li>\n<li>Uses <strong>Application Default Credentials (ADC)<\/strong> for authentication from many environments.<\/li>\n<li>Supports least-privilege via predefined roles (details in prerequisites and security sections).<\/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>Accessed through Google Cloud APIs over HTTPS.<\/li>\n<li>If your environment is in a restricted VPC setup, you may need to consider:<\/li>\n<li>Private Google Access<\/li>\n<li>VPC Service Controls (perimeter restrictions)<\/li>\n<li>Organization policy constraints<br\/>\n  Exact networking implications depend on where your code runs (Workbench, GKE, Cloud Run, on-prem). Verify with your org\u2019s network policies.<\/li>\n<\/ul>\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><strong>Cloud Audit Logs<\/strong> can record who created\/updated resources (subject to configuration).<\/li>\n<li><strong>Cloud Logging<\/strong> captures logs from training jobs and pipelines; experiment metadata is separate but operational activity is still auditable.<\/li>\n<li>Use naming\/labels conventions for:<\/li>\n<li>Experiments (<code>team-problem-version<\/code>)<\/li>\n<li>Runs (<code>date-commit-shortsha-tryN<\/code>)<\/li>\n<li>Parameters (<code>dataset_id<\/code>, <code>split_seed<\/code>, <code>commit_sha<\/code>, <code>image_digest<\/code>)<\/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[Notebook \/ Script \/ CI Job] --&gt;|Vertex AI SDK| B[Vertex AI Experiments]\n  A --&gt;|logs params &amp; metrics| B\n  B --&gt; C[Vertex AI Console&lt;br\/&gt;Compare Runs]\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 Dev[\"Development\"]\n    W[Vertex AI Workbench Notebook]\n    G[Git Repo]\n  end\n\n  subgraph CI[\"CI\/CD\"]\n    CB[Cloud Build \/ GitHub Actions]\n  end\n\n  subgraph Train[\"Training &amp; Pipelines\"]\n    P[Vertex AI Pipelines]\n    TJ[Vertex AI Training Jobs]\n  end\n\n  subgraph Track[\"Tracking &amp; Governance\"]\n    E[Vertex AI Experiments]\n    MR[Vertex AI Model Registry]\n  end\n\n  subgraph Data[\"Data Layer\"]\n    BQ[BigQuery]\n    GCS[Cloud Storage]\n  end\n\n  W --&gt;|reads| BQ\n  W --&gt;|reads\/writes| GCS\n  W --&gt;|commit| G\n\n  CB --&gt;|build container \/ run eval| TJ\n  CB --&gt;|trigger| P\n\n  P --&gt; TJ\n  TJ --&gt;|outputs| GCS\n  TJ --&gt;|register model| MR\n\n  W --&gt;|log runs| E\n  TJ --&gt;|log metrics\/params| E\n  P --&gt;|log pipeline params| E\n\n  E --&gt;|compare &amp; select| MR\n<\/code><\/pre>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">8. Prerequisites<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Account\/project requirements<\/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>Recommended: use a dedicated project for this lab (to simplify cleanup and cost control).<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Permissions \/ IAM roles<\/h3>\n\n\n\n<p>You need permissions to use Vertex AI and write experiment metadata. Common roles (choose least privilege that works):\n&#8211; <code>roles\/aiplatform.user<\/code> (typical for using Vertex AI resources)\n&#8211; <code>roles\/aiplatform.viewer<\/code> (read-only)\n&#8211; If using service accounts and token generation in automation: <code>roles\/iam.serviceAccountUser<\/code> on the target service account\n&#8211; For enabling APIs: <code>roles\/serviceusage.serviceUsageAdmin<\/code> (or project owner\/admin)<\/p>\n\n\n\n<p>Exact permissions for Experiments can vary by workflow and organization constraints. Verify in IAM docs:\n&#8211; Vertex AI IAM overview: https:\/\/cloud.google.com\/vertex-ai\/docs\/general\/access-control<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Billing requirements<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Enabling and using Vertex AI may incur charges depending on what else you run (training, pipelines, storage).<\/li>\n<li>This tutorial is designed to be low-cost by logging a lightweight experiment run without launching paid training infrastructure.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">CLI\/SDK\/tools needed<\/h3>\n\n\n\n<p>Choose one environment:\n&#8211; <strong>Cloud Shell<\/strong> (recommended for quick labs; includes <code>gcloud<\/code>)\n&#8211; Local terminal with:\n  &#8211; <code>gcloud<\/code> CLI installed: https:\/\/cloud.google.com\/sdk\/docs\/install\n  &#8211; Python 3.9+ (practical baseline; verify current supported versions)\n  &#8211; <code>pip<\/code> to install the Vertex AI Python SDK<\/p>\n\n\n\n<p>Python SDK:\n&#8211; https:\/\/cloud.google.com\/python\/docs\/reference\/aiplatform\/latest<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Region availability<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Vertex AI is regional. Use a supported Vertex AI region such as <code>us-central1<\/code>.<\/li>\n<li>Verify current locations: https:\/\/cloud.google.com\/vertex-ai\/docs\/general\/locations<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Quotas\/limits<\/h3>\n\n\n\n<p>Potential quota considerations:\n&#8211; Vertex AI API request quotas\n&#8211; Metadata-related quotas (if applicable)\n&#8211; Project-wide API quotas and rate limits<\/p>\n\n\n\n<p>Check quotas in the Google Cloud Console:\n&#8211; IAM &amp; Admin \u2192 Quotas (or APIs &amp; Services \u2192 Quotas)<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Prerequisite services<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Enable the <strong>Vertex AI API<\/strong> for your project:<\/li>\n<li><code>aiplatform.googleapis.com<\/code><\/li>\n<\/ul>\n\n\n\n<p>Optional, depending on your broader workflow:\n&#8211; Cloud Storage API (for artifacts)\n&#8211; Artifact Registry (for containers)\n&#8211; BigQuery (for datasets)\n&#8211; Vertex AI Pipelines (if you use pipelines)<\/p>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">9. Pricing \/ Cost<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Pricing model (what you are billed for)<\/h3>\n\n\n\n<p>Vertex AI Experiments is primarily a <strong>tracking\/metadata capability<\/strong>. In many real deployments, the main costs come from the <strong>workloads you run<\/strong> (training\/pipelines\/notebooks) and the <strong>storage\/observability services<\/strong> you use alongside experiments.<\/p>\n\n\n\n<p>To price this accurately, focus on these cost dimensions:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>\n<p><strong>Vertex AI compute you run<\/strong>\n   &#8211; Custom training jobs (CPU\/GPU\/TPU time)\n   &#8211; Pipeline execution (orchestrating components + component compute)\n   &#8211; Workbench instances (VM runtime)\n   &#8211; Batch prediction jobs and online endpoints (if part of workflow)<\/p>\n<\/li>\n<li>\n<p><strong>Storage<\/strong>\n   &#8211; Cloud Storage for datasets, model artifacts, evaluation outputs, logs\n   &#8211; (Optional) Vertex AI TensorBoard storage\/ingestion if you enable it for runs (verify SKUs on pricing page)<\/p>\n<\/li>\n<li>\n<p><strong>Network egress<\/strong>\n   &#8211; Data transfer out of Google Cloud or between regions can add costs.\n   &#8211; Keep training data and training compute in the same region when possible.<\/p>\n<\/li>\n<li>\n<p><strong>Logging\/monitoring<\/strong>\n   &#8211; Cloud Logging ingestion\/retention beyond free allotments (varies)\n   &#8211; Cloud Monitoring metrics (varies)<\/p>\n<\/li>\n<\/ol>\n\n\n\n<p>Because pricing and SKUs change, rely on official sources:\n&#8211; Vertex AI pricing: https:\/\/cloud.google.com\/vertex-ai\/pricing\n&#8211; Google Cloud Pricing Calculator: https:\/\/cloud.google.com\/products\/calculator<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Free tier (if applicable)<\/h3>\n\n\n\n<p>Google Cloud often provides free usage tiers for some services (like limited Cloud Logging) but <strong>do not assume<\/strong> a dedicated free tier for Vertex AI Experiments tracking itself. Treat it as:\n&#8211; Potentially low-cost for metadata-only usage\n&#8211; But not guaranteed to be \u201cfree\u201d under all configurations<br\/>\n<strong>Verify in official docs\/pricing<\/strong> for any explicit free allowances related to metadata tracking.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Cost drivers for Vertex AI Experiments workflows<\/h3>\n\n\n\n<p>Even if experiment tracking itself is lightweight, total cost is dominated by:\n&#8211; Number and duration of training runs\n&#8211; GPU\/TPU usage\n&#8211; Size of training data and artifact outputs\n&#8211; Frequency of pipeline runs\n&#8211; TensorBoard log volume (if used)\n&#8211; Cross-region data movement<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Hidden or indirect costs<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Artifact sprawl<\/strong>: frequent runs can create many model checkpoints and evaluation files in Cloud Storage.<\/li>\n<li><strong>Large logs<\/strong>: verbose training logs can inflate Cloud Logging costs.<\/li>\n<li><strong>Experiment proliferation<\/strong>: poor governance can lead to long-term storage and management overhead.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Network\/data transfer implications<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Keep <strong>data, training, and tracking in the same region<\/strong> when possible.<\/li>\n<li>Watch out for:<\/li>\n<li>Pulling large datasets from on-prem to cloud repeatedly<\/li>\n<li>Using multi-region buckets with regional compute (may be fine, but verify performance\/cost tradeoffs)<\/li>\n<\/ul>\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>Start with <strong>metadata-only<\/strong> logging; avoid launching managed training for simple comparisons.<\/li>\n<li>Use <strong>small samples<\/strong> for initial experiments; scale up only for shortlisted candidates.<\/li>\n<li>Set <strong>lifecycle policies<\/strong> on Cloud Storage buckets storing experiment artifacts (delete old checkpoints).<\/li>\n<li>Reduce Cloud Logging verbosity; log essential metrics to experiments rather than huge text logs.<\/li>\n<li>For pipelines: cache components where appropriate (pipeline caching strategy depends on your workflow; verify pipeline caching behavior in official docs).<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Example low-cost starter estimate<\/h3>\n\n\n\n<p>A realistic \u201cstarter\u201d setup can be close to zero incremental spend if:\n&#8211; You only run a small Python script in Cloud Shell or an already-running environment\n&#8211; You log a small number of parameters\/metrics\n&#8211; You do not run paid training infrastructure<\/p>\n\n\n\n<p>However, there may still be minimal indirect costs depending on your project configuration and any enabled add-ons. <strong>Verify billing reports<\/strong> after the lab.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Example production cost considerations<\/h3>\n\n\n\n<p>In production, cost planning should include:\n&#8211; Training budget (per model retraining schedule, per environment: dev\/stage\/prod)\n&#8211; Artifact storage and retention\n&#8211; Observability retention and analysis\n&#8211; Security controls overhead (VPC-SC, CMEK usage where applicable across dependent services)<\/p>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">10. Step-by-Step Hands-On Tutorial<\/h2>\n\n\n\n<p>This lab logs an experiment and a run using the Vertex AI Python SDK, then validates it in the Vertex AI Console. It does <strong>not<\/strong> start a managed training job, so it is designed to be low-cost.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Objective<\/h3>\n\n\n\n<p>Create a Vertex AI experiment called <code>experiment-tracking-lab<\/code>, log a run with parameters and metrics from a simple Python script, then view and verify the run in the Vertex AI Console.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Lab Overview<\/h3>\n\n\n\n<p>You will:\n1. Select a Google Cloud project and region.\n2. Enable the Vertex AI API.\n3. Install the Vertex AI Python SDK.\n4. Create an experiment and run, log parameters and metrics.\n5. Verify the results in the Console and via SDK.\n6. Clean up (optional: delete experiment\/runs if your environment supports deletion; at minimum remove local files and confirm no paid resources were created).<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Step 1: Set project and region<\/h3>\n\n\n\n<p><strong>In Cloud Shell<\/strong> (https:\/\/shell.cloud.google.com\/) or your terminal with <code>gcloud<\/code> configured:<\/p>\n\n\n\n<pre><code class=\"language-bash\">gcloud auth login\ngcloud config set project YOUR_PROJECT_ID\ngcloud config set ai\/region us-central1\n<\/code><\/pre>\n\n\n\n<p>Expected outcome:\n&#8211; Your active project is set to <code>YOUR_PROJECT_ID<\/code>.\n&#8211; Your default Vertex AI region is set to <code>us-central1<\/code>.<\/p>\n\n\n\n<p>Verification:<\/p>\n\n\n\n<pre><code class=\"language-bash\">gcloud config list --format=\"text(core.project,ai.region)\"\n<\/code><\/pre>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h3 class=\"wp-block-heading\">Step 2: Enable the Vertex AI API<\/h3>\n\n\n\n<p>Enable the core API used by Vertex AI services:<\/p>\n\n\n\n<pre><code class=\"language-bash\">gcloud services enable aiplatform.googleapis.com\n<\/code><\/pre>\n\n\n\n<p>Expected outcome:\n&#8211; API enablement completes successfully.<\/p>\n\n\n\n<p>Verification:<\/p>\n\n\n\n<pre><code class=\"language-bash\">gcloud services list --enabled --filter=\"name:aiplatform.googleapis.com\"\n<\/code><\/pre>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h3 class=\"wp-block-heading\">Step 3: Prepare a Python environment and install the Vertex AI SDK<\/h3>\n\n\n\n<p>In Cloud Shell, Python is available. Create a virtual environment:<\/p>\n\n\n\n<pre><code class=\"language-bash\">python3 -m venv .venv\nsource .venv\/bin\/activate\npython -m pip install --upgrade pip\npython -m pip install google-cloud-aiplatform\n<\/code><\/pre>\n\n\n\n<p>Expected outcome:\n&#8211; The <code>google-cloud-aiplatform<\/code> package installs without errors.<\/p>\n\n\n\n<p>Verification:<\/p>\n\n\n\n<pre><code class=\"language-bash\">python -c \"import google.cloud.aiplatform as aiplatform; print(aiplatform.__version__)\"\n<\/code><\/pre>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h3 class=\"wp-block-heading\">Step 4: Create and run an experiment logging script<\/h3>\n\n\n\n<p>Create a file named <code>vertex_ai_experiments_lab.py<\/code>:<\/p>\n\n\n\n<pre><code class=\"language-bash\">cat &gt; vertex_ai_experiments_lab.py &lt;&lt;'PY'\nimport time\nimport os\nfrom datetime import datetime, timezone\n\nfrom google.cloud import aiplatform\n\nPROJECT_ID = os.environ.get(\"GOOGLE_CLOUD_PROJECT\")  # Cloud Shell sets this\nLOCATION = os.environ.get(\"VERTEX_LOCATION\", \"us-central1\")\n\nEXPERIMENT_NAME = \"experiment-tracking-lab\"\nRUN_NAME = f\"run-{datetime.now(timezone.utc).strftime('%Y%m%d-%H%M%S')}\"\n\ndef main():\n    # Initialize the Vertex AI SDK context\n    aiplatform.init(project=PROJECT_ID, location=LOCATION)\n\n    # Create or load the experiment\n    experiment = aiplatform.Experiment(EXPERIMENT_NAME)\n    experiment.create()\n    print(f\"Using experiment: {EXPERIMENT_NAME}\")\n\n    # Start a run and log parameters\/metrics\n    aiplatform.start_run(RUN_NAME)\n    print(f\"Started run: {RUN_NAME}\")\n\n    # Parameters: anything you need for reproducibility\n    aiplatform.log_params({\n        \"model_type\": \"demo-linear\",\n        \"learning_rate\": 0.05,\n        \"num_epochs\": 5,\n        \"dataset_version\": \"synthetic-v1\",\n        \"split_seed\": 42,\n    })\n\n    # Simulate training and log metrics over time\n    for epoch in range(1, 6):\n        # fake \"loss\" decreasing and \"accuracy\" increasing\n        loss = 1.0 \/ epoch\n        accuracy = 0.5 + (epoch * 0.08)\n\n        aiplatform.log_metrics({\n            \"epoch\": epoch,\n            \"loss\": loss,\n            \"accuracy\": accuracy,\n        })\n        print(f\"epoch={epoch} loss={loss:.4f} accuracy={accuracy:.4f}\")\n        time.sleep(0.5)\n\n    aiplatform.end_run()\n    print(\"Run ended.\")\n\n    # Query back the runs for this experiment (basic verification)\n    runs = experiment.list_runs()\n    print(f\"Found {len(runs)} runs for experiment '{EXPERIMENT_NAME}'. Recent runs:\")\n    for r in runs[:5]:\n        # The exact fields available may change; print resource name as a stable identifier.\n        print(getattr(r, \"resource_name\", str(r)))\n\nif __name__ == \"__main__\":\n    if not PROJECT_ID:\n        raise RuntimeError(\"GOOGLE_CLOUD_PROJECT is not set. Set PROJECT_ID explicitly.\")\n    main()\nPY\n<\/code><\/pre>\n\n\n\n<p>Expected outcome:\n&#8211; The script is created locally and includes:\n  &#8211; SDK initialization\n  &#8211; experiment creation\n  &#8211; a run with parameter logging\n  &#8211; metric logging over epochs<\/p>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h3 class=\"wp-block-heading\">Step 5: Run the script to log an experiment run<\/h3>\n\n\n\n<p>Run:<\/p>\n\n\n\n<pre><code class=\"language-bash\">export VERTEX_LOCATION=\"us-central1\"\npython vertex_ai_experiments_lab.py\n<\/code><\/pre>\n\n\n\n<p>Expected outcome:\n&#8211; You see console output for epochs, and the script completes with \u201cRun ended.\u201d\n&#8211; A run is logged under the <code>experiment-tracking-lab<\/code> experiment.<\/p>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h3 class=\"wp-block-heading\">Step 6: View the experiment in the Vertex AI Console<\/h3>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Open Google Cloud Console: https:\/\/console.cloud.google.com\/<\/li>\n<li>Go to <strong>Vertex AI<\/strong>.<\/li>\n<li>Find <strong>Experiments<\/strong> (navigation labels may vary slightly over time).<\/li>\n<li>Select <code>experiment-tracking-lab<\/code>.<\/li>\n<li>Open the most recent run.<\/li>\n<li>Confirm you can see:\n   &#8211; Parameters: <code>learning_rate<\/code>, <code>num_epochs<\/code>, etc.\n   &#8211; Metrics: <code>loss<\/code>, <code>accuracy<\/code> (and the logged <code>epoch<\/code> value)<\/li>\n<\/ol>\n\n\n\n<p>Expected outcome:\n&#8211; The run appears with the logged parameters and metrics.<\/p>\n\n\n\n<p>If you don\u2019t see the experiment:\n&#8211; Confirm the <strong>project<\/strong> and <strong>region<\/strong> in the console match what you used in the SDK (<code>us-central1<\/code> and your project).<\/p>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h3 class=\"wp-block-heading\">Step 7: (Optional) Add reproducibility metadata<\/h3>\n\n\n\n<p>In real teams, add at least:\n&#8211; Git commit SHA\n&#8211; Container image digest (if training in containers)\n&#8211; Dataset snapshot reference (BigQuery snapshot, GCS generation ID, or a data version)\n&#8211; Evaluation dataset ID and metrics definition version<\/p>\n\n\n\n<p>You can log these as parameters:<\/p>\n\n\n\n<pre><code class=\"language-python\">aiplatform.log_params({\n  \"commit_sha\": \"abc1234\",\n  \"training_image\": \"us-docker.pkg.dev\/PROJECT\/REPO\/IMAGE@sha256:...\",\n  \"bq_training_table\": \"project.dataset.table@1700000000000\",\n})\n<\/code><\/pre>\n\n\n\n<p>Expected outcome:\n&#8211; Runs become explainable and reproducible across time and team members.<\/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>API enabled<\/strong>\n   &#8211; <code>aiplatform.googleapis.com<\/code> enabled in the project<\/p>\n<\/li>\n<li>\n<p><strong>Script succeeded<\/strong>\n   &#8211; No exceptions; run ended cleanly<\/p>\n<\/li>\n<li>\n<p><strong>Console visibility<\/strong>\n   &#8211; <code>experiment-tracking-lab<\/code> exists\n   &#8211; Latest run contains parameters and metrics<\/p>\n<\/li>\n<li>\n<p><strong>SDK query works<\/strong>\n   &#8211; Script prints <code>Found N runs...<\/code><\/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<h4 class=\"wp-block-heading\">Error: <code>403 Permission denied<\/code><\/h4>\n\n\n\n<p><strong>Cause<\/strong>: Your identity lacks required Vertex AI permissions in the project\/region.<br\/>\n<strong>Fix<\/strong>:\n&#8211; Ask an admin to grant <code>roles\/aiplatform.user<\/code> (or appropriate least-privilege role).\n&#8211; Confirm you are in the correct project: <code>gcloud config get-value project<\/code>.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Error: <code>400<\/code> \/ \u201cLocation not supported\u201d<\/h4>\n\n\n\n<p><strong>Cause<\/strong>: Region mismatch or an unsupported Vertex AI location.<br\/>\n<strong>Fix<\/strong>:\n&#8211; Use a known supported region like <code>us-central1<\/code>.\n&#8211; Verify locations: https:\/\/cloud.google.com\/vertex-ai\/docs\/general\/locations<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Experiment does not appear in Console<\/h4>\n\n\n\n<p><strong>Cause<\/strong>: Console region selector differs from SDK location.<br\/>\n<strong>Fix<\/strong>:\n&#8211; In Vertex AI Console, select the same region used in code.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Package import errors<\/h4>\n\n\n\n<p><strong>Cause<\/strong>: Virtual environment not activated or dependency conflict.<br\/>\n<strong>Fix<\/strong>:<\/p>\n\n\n\n<pre><code class=\"language-bash\">source .venv\/bin\/activate\npython -m pip install --upgrade google-cloud-aiplatform\n<\/code><\/pre>\n\n\n\n<h4 class=\"wp-block-heading\">Metrics not shown as expected<\/h4>\n\n\n\n<p><strong>Cause<\/strong>: UI display can differ by metric types and logging patterns; or you\u2019re viewing a different run.<br\/>\n<strong>Fix<\/strong>:\n&#8211; Confirm run name and timestamp.\n&#8211; Log scalar metrics consistently and verify in list view and run details.<\/p>\n\n\n\n<p>If behavior differs from this guide, <strong>verify in official docs<\/strong> for updated SDK methods and UI:\n&#8211; https:\/\/cloud.google.com\/vertex-ai\/docs\/experiments\/intro\n&#8211; https:\/\/cloud.google.com\/python\/docs\/reference\/aiplatform\/latest<\/p>\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>This lab intentionally avoids starting expensive resources. Still, do the following:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>Deactivate virtual environment<\/strong><\/li>\n<\/ol>\n\n\n\n<pre><code class=\"language-bash\">deactivate || true\n<\/code><\/pre>\n\n\n\n<ol class=\"wp-block-list\" start=\"2\">\n<li><strong>Remove local files (optional)<\/strong><\/li>\n<\/ol>\n\n\n\n<pre><code class=\"language-bash\">rm -rf .venv vertex_ai_experiments_lab.py\n<\/code><\/pre>\n\n\n\n<ol class=\"wp-block-list\" start=\"3\">\n<li>\n<p><strong>Billing review<\/strong>\n&#8211; Go to Billing \u2192 Reports and confirm no unexpected spend.<\/p>\n<\/li>\n<li>\n<p><strong>Experiment deletion<\/strong>\nDeletion behavior for experiments\/runs can vary by product evolution and may not always be exposed as a simple \u201cdelete\u201d in UI\/SDK. If your environment supports deletion, use the official docs to remove experiment resources. If not, keep the experiment in a dedicated lab project and delete the project when done.<\/p>\n<\/li>\n<\/ol>\n\n\n\n<hr class=\"wp-block-separator\" \/>\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>Treat experiment tracking as part of the <strong>ML system design<\/strong>, not an afterthought.<\/li>\n<li>Standardize the lifecycle:\n  1) create experiment per initiative\/version<br\/>\n  2) create runs per training\/evaluation attempt<br\/>\n  3) promote best run \u2192 register model \u2192 deploy<\/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>Use <strong>service accounts<\/strong> for automated runs (pipelines\/CI), not user credentials.<\/li>\n<li>Grant least privilege:<\/li>\n<li>Start with <code>roles\/aiplatform.user<\/code> and reduce if you can (verify required permissions).<\/li>\n<li>Separate environments:<\/li>\n<li>dev\/stage\/prod projects<\/li>\n<li>separate experiments per environment to prevent cross-environment confusion<\/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>Log only meaningful metrics and metadata.<\/li>\n<li>Add Cloud Storage lifecycle rules for training outputs (checkpoints, logs).<\/li>\n<li>Avoid cross-region data movement: keep datasets and compute co-located.<\/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>Prefer logging \u201ckey metrics\u201d (e.g., final validation AUC) rather than excessively granular metrics for every step unless needed.<\/li>\n<li>For large-scale training, log summary metrics and store raw step-level logs in Cloud Storage (or TensorBoard if appropriate and cost-justified).<\/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>Wrap experiment logging so training success doesn\u2019t depend on tracking availability:<\/li>\n<li>If experiment logging fails, decide whether to fail training (strict) or continue (best-effort).<\/li>\n<li>Ensure run \u201cend\u201d is called using <code>try\/finally<\/code> patterns in your code.<\/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>Use naming standards:<\/li>\n<li>Experiment: <code>team-project-problem-vN<\/code><\/li>\n<li>Run: <code>yyyymmdd-hhmm-commit-shortsha<\/code><\/li>\n<li>Record operational metadata:<\/li>\n<li>machine type \/ accelerator type<\/li>\n<li>runtime duration<\/li>\n<li>dataset snapshot\/version<\/li>\n<li>code version<\/li>\n<li>Periodically archive or deprecate old experiments.<\/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>Maintain a minimal schema for all runs:<\/li>\n<li><code>owner<\/code>, <code>team<\/code>, <code>cost_center<\/code> (if applicable)<\/li>\n<li><code>dataset_version<\/code>, <code>commit_sha<\/code><\/li>\n<li><code>model_framework<\/code>, <code>framework_version<\/code><\/li>\n<li>Document metric definitions so comparisons are meaningful.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator\" \/>\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<ul class=\"wp-block-list\">\n<li>Vertex AI uses <strong>Google Cloud IAM<\/strong>.<\/li>\n<li>Use:<\/li>\n<li><strong>User identities<\/strong> for interactive development<\/li>\n<li><strong>Service accounts<\/strong> for automation (pipelines, schedulers, CI)<\/li>\n<\/ul>\n\n\n\n<p>Key principle: ensure only authorized identities can write to experiments and read sensitive metadata.<\/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 Google Cloud is encrypted at rest by default.<\/li>\n<li>For stronger controls, many teams use <strong>CMEK<\/strong> (Customer-Managed Encryption Keys) for dependent storage\/services where supported (Cloud Storage, some Vertex AI resources).<br\/>\nFor experiment tracking metadata specifically, CMEK applicability may differ\u2014<strong>verify in official docs<\/strong> for your exact resource types.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Network exposure<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>API calls are made over HTTPS to Google APIs.<\/li>\n<li>In enterprise networks:<\/li>\n<li>Use Private Google Access where appropriate<\/li>\n<li>Consider VPC Service Controls to reduce data exfiltration risk<\/li>\n<li>Restrict egress in environments that log experiments from private networks<\/li>\n<\/ul>\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 store secrets (API keys, passwords) in experiment parameters.<\/li>\n<li>Use Secret Manager for secrets and log only <strong>secret identifiers<\/strong> if needed.<\/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 can provide \u201cwho did what\u201d for many Google Cloud services.<\/li>\n<li>Ensure your org\u2019s audit logging is enabled and retained according to policy.<\/li>\n<li>Correlate:<\/li>\n<li>training job logs (Cloud Logging)<\/li>\n<li>experiment run records (Vertex AI)<\/li>\n<li>model registry changes (Vertex AI)<\/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>Track dataset lineage and approval status:<\/li>\n<li>Log dataset snapshot IDs and any consent\/approval tags.<\/li>\n<li>Avoid logging sensitive PII in parameters\/metrics.<\/li>\n<li>Keep environments separated and apply org policies to restrict where workloads run.<\/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>Logging raw data samples (PII) as parameters or artifacts.<\/li>\n<li>Allowing broad roles (Owner\/Editor) to many users.<\/li>\n<li>Mixing dev and prod experiment tracking in the same project.<\/li>\n<li>Using user credentials in pipelines (hard to audit and rotate).<\/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 dedicated service accounts for:<\/li>\n<li>training<\/li>\n<li>evaluation<\/li>\n<li>promotion\/deployment<\/li>\n<li>Apply least privilege and separation of duties:<\/li>\n<li>Data scientists can log experiments<\/li>\n<li>Release managers can promote to production model registry\/endpoints<\/li>\n<li>Keep artifacts in controlled Cloud Storage buckets with:<\/li>\n<li>uniform bucket-level access<\/li>\n<li>CMEK (if required)<\/li>\n<li>lifecycle rules<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">13. Limitations and Gotchas<\/h2>\n\n\n\n<blockquote>\n<p>These are common real-world issues; verify current limits\/behavior in official docs and your region.<\/p>\n<\/blockquote>\n\n\n\n<h3 class=\"wp-block-heading\">Known limitations (practical)<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Not a full replacement<\/strong> for all third-party experiment platforms:<\/li>\n<li>advanced custom dashboards, artifact diffing, or cross-cloud federation may be limited.<\/li>\n<li><strong>Meaningful comparisons require discipline<\/strong>:<\/li>\n<li>if teams log inconsistent metric definitions, results become misleading.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Quotas<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>API request quotas and metadata throughput may apply.<\/li>\n<li>Very high-frequency metric logging can hit rate limits.<br\/>\nRecommendation: log aggregated metrics (per epoch) rather than per step for long runs unless necessary.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Regional constraints<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Experiments are associated with Vertex AI locations. If you run multi-region training, plan how you separate or consolidate experiment tracking.<\/li>\n<li>Cross-region comparisons may be operationally harder; prefer standardizing to a primary region per environment when possible.<\/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>Experiment tracking itself is rarely the main cost, but:<\/li>\n<li>training jobs and GPUs dominate spend<\/li>\n<li>artifact storage grows quickly<\/li>\n<li>TensorBoard logging volume can become expensive if you log large event files (verify pricing SKUs)<\/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>SDK method names and behaviors can change between versions.<\/li>\n<li>Pin a known-good version of <code>google-cloud-aiplatform<\/code> for production pipelines and upgrade deliberately.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Operational gotchas<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Run finalization<\/strong>: if your code crashes before <code>end_run()<\/code>, the run may remain open\/incomplete. Use <code>try\/finally<\/code>.<\/li>\n<li><strong>Project\/region mismatch<\/strong> is the most common reason experiments \u201cdisappear\u201d in the console.<\/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>If migrating from MLflow or another platform:<\/li>\n<li>define a mapping for run IDs, metric names, and parameter naming conventions<\/li>\n<li>decide whether to backfill historical runs (often not worth it unless required)<\/li>\n<li>consider keeping the old system as the \u201csystem of record\u201d during transition<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Vendor-specific nuances<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Vertex AI Experiments is deeply aligned with Vertex AI\u2019s resource model and IAM. That is a strength on Google Cloud, but it can be friction for hybrid or multi-cloud strategies.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">14. Comparison with Alternatives<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Nearest services in Google Cloud<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Vertex AI Metadata<\/strong>: underlying lineage\/metadata foundation (Experiments is a user-facing pattern on top of metadata concepts).<\/li>\n<li><strong>Vertex AI TensorBoard<\/strong>: training visualization and metrics; can complement experiments.<\/li>\n<li><strong>Vertex AI Pipelines<\/strong>: orchestration; pipelines can log experiments as part of runs.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Nearest services in other clouds<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>AWS SageMaker Experiments<\/strong><\/li>\n<li><strong>Azure Machine Learning (ML) experiment tracking \/ MLflow integration<\/strong> (capabilities vary by Azure ML version and configuration\u2014verify current Azure docs)<\/li>\n<li>Third-party platforms often used across clouds: W&amp;B, MLflow<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Open-source\/self-managed alternatives<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>MLflow Tracking<\/strong> (self-hosted on GKE\/VM)<\/li>\n<li><strong>TensorBoard<\/strong> (self-hosted)<\/li>\n<li><strong>Custom tracking<\/strong> (BigQuery tables + dashboards)<\/li>\n<\/ul>\n\n\n\n<p>Comparison table:<\/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>Vertex AI Experiments (Google Cloud)<\/td>\n<td>Teams on Vertex AI needing managed tracking<\/td>\n<td>Native IAM\/audit alignment; console comparison; easy SDK logging<\/td>\n<td>Less portable across clouds; feature depth depends on Vertex AI roadmap<\/td>\n<td>You\u2019re standardizing on Google Cloud Vertex AI<\/td>\n<\/tr>\n<tr>\n<td>Vertex AI TensorBoard<\/td>\n<td>Deep training visualization<\/td>\n<td>Great for training curves, model debug<\/td>\n<td>Not a full experiment governance system by itself<\/td>\n<td>You need detailed training visualization alongside experiments<\/td>\n<\/tr>\n<tr>\n<td>Vertex AI Pipelines (with metadata)<\/td>\n<td>Production orchestration<\/td>\n<td>Reproducible pipelines; parameterized runs<\/td>\n<td>More setup than ad-hoc scripts<\/td>\n<td>You\u2019re moving from notebooks to production pipelines<\/td>\n<\/tr>\n<tr>\n<td>MLflow Tracking (self-managed)<\/td>\n<td>Cloud-agnostic tracking<\/td>\n<td>Portable; many integrations<\/td>\n<td>You operate and secure it; scaling and governance are your problem<\/td>\n<td>Multi-cloud or platform-agnostic strategy<\/td>\n<\/tr>\n<tr>\n<td>Weights &amp; Biases (SaaS\/enterprise)<\/td>\n<td>Rich experiment dashboards<\/td>\n<td>Strong UI, collaboration, artifacts<\/td>\n<td>Additional vendor and cost; data governance considerations<\/td>\n<td>You want advanced experiment UX and have procurement\/security approval<\/td>\n<\/tr>\n<tr>\n<td>AWS SageMaker Experiments<\/td>\n<td>AWS-centric teams<\/td>\n<td>Native to SageMaker ecosystem<\/td>\n<td>Not integrated with Vertex AI; different IAM model<\/td>\n<td>You are primarily on AWS<\/td>\n<\/tr>\n<tr>\n<td>Azure ML experiment tracking<\/td>\n<td>Azure-centric teams<\/td>\n<td>Integration with Azure ML<\/td>\n<td>Service behavior differs by version; confirm feature parity<\/td>\n<td>You are primarily on Azure<\/td>\n<\/tr>\n<\/tbody>\n<\/table><\/figure>\n\n\n\n<hr class=\"wp-block-separator\" \/>\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 credit risk model iteration<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Problem<\/strong>: A bank retrains credit risk models monthly. Auditors require traceability: which dataset snapshot, which code version, which hyperparameters, and which evaluation metrics led to production deployment.<\/li>\n<li><strong>Proposed architecture<\/strong>:<\/li>\n<li>Data in BigQuery with snapshot\/version references<\/li>\n<li>Training orchestrated via Vertex AI Pipelines<\/li>\n<li>Each pipeline run logs:<ul>\n<li>dataset snapshot ID<\/li>\n<li>git commit SHA \/ container digest<\/li>\n<li>hyperparameters<\/li>\n<li>final evaluation metrics and fairness checks<\/li>\n<\/ul>\n<\/li>\n<li>Candidate model registered in Vertex AI Model Registry<\/li>\n<li>Promotion to production gated by approval workflow<\/li>\n<li>All activity governed by IAM and audited via Cloud Audit Logs<\/li>\n<li><strong>Why Vertex AI Experiments was chosen<\/strong>:<\/li>\n<li>Integrated with Vertex AI workflows and IAM<\/li>\n<li>Provides consistent run records without hosting a tracking service<\/li>\n<li>Supports operational visibility and compliance reporting patterns<\/li>\n<li><strong>Expected outcomes<\/strong>:<\/li>\n<li>Faster audit evidence gathering<\/li>\n<li>Reduced \u201cunknown\u201d training settings<\/li>\n<li>Standardized metrics reporting across teams<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Startup\/small-team example: rapid churn model improvements<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Problem<\/strong>: A startup iterates weekly on churn prediction. They need quick comparisons without maintaining extra infrastructure.<\/li>\n<li><strong>Proposed architecture<\/strong>:<\/li>\n<li>Data in BigQuery<\/li>\n<li>Training scripts run from Vertex AI Workbench (initially), later moved to managed training jobs<\/li>\n<li>Vertex AI Experiments logs parameters\/metrics for each iteration<\/li>\n<li>Best run becomes a model version in Model Registry<\/li>\n<li><strong>Why Vertex AI Experiments was chosen<\/strong>:<\/li>\n<li>Low operational overhead<\/li>\n<li>Easy to integrate into notebooks and scripts<\/li>\n<li>Keeps experiment history accessible to the whole team<\/li>\n<li><strong>Expected outcomes<\/strong>:<\/li>\n<li>Faster iteration cycles and fewer repeated mistakes<\/li>\n<li>Clear record of what improved (feature set, hyperparameters, data snapshot)<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">16. FAQ<\/h2>\n\n\n\n<ol class=\"wp-block-list\">\n<li>\n<p><strong>Is Vertex AI Experiments a separate product from Vertex AI?<\/strong><br\/>\n   It is a capability within Vertex AI for tracking experiments\/runs. You access it through Vertex AI Console and the Vertex AI SDK.<\/p>\n<\/li>\n<li>\n<p><strong>Do I need to run training jobs on Vertex AI to use Vertex AI Experiments?<\/strong><br\/>\n   No. You can log runs from a Python script or notebook as long as it can authenticate to Google Cloud and call the Vertex AI APIs. Many teams log from Vertex AI Training\/Pipelines for consistency, but it\u2019s not mandatory.<\/p>\n<\/li>\n<li>\n<p><strong>What\u2019s the difference between an experiment and a run?<\/strong><br\/>\n   An <strong>experiment<\/strong> groups related work (e.g., \u201cfraud-model-v2\u201d). A <strong>run<\/strong> is a single trial within that experiment with specific parameters and resulting metrics.<\/p>\n<\/li>\n<li>\n<p><strong>What should I log as parameters?<\/strong><br\/>\n   Log anything needed to reproduce the run: hyperparameters, dataset version\/snapshot, split seed, feature set version, code version (git SHA), container image digest, and evaluation dataset identifier.<\/p>\n<\/li>\n<li>\n<p><strong>What metrics should I log?<\/strong><br\/>\n   Log primary selection metrics (AUC, F1, RMSE), plus secondary operational metrics (training time, model size, inference latency measurements if you capture them).<\/p>\n<\/li>\n<li>\n<p><strong>Can I compare runs in the Google Cloud Console?<\/strong><br\/>\n   Yes. Vertex AI Console provides an Experiments UI where you can filter\/sort runs and view parameters\/metrics. UI details can change\u2014verify in current console navigation.<\/p>\n<\/li>\n<li>\n<p><strong>Can Vertex AI Experiments replace MLflow?<\/strong><br\/>\n   It depends. For teams fully on Google Cloud and Vertex AI, it can cover core experiment tracking needs. If you require MLflow\u2019s ecosystem portability or specific plugins, you may keep MLflow or use a hybrid approach.<\/p>\n<\/li>\n<li>\n<p><strong>How do I ensure reproducibility?<\/strong><br\/>\n   Enforce a required set of run parameters (dataset snapshot, commit SHA, environment versions). Use deterministic splits and log seeds.<\/p>\n<\/li>\n<li>\n<p><strong>Does it support time-series metrics (per epoch\/step)?<\/strong><br\/>\n   You can log metrics repeatedly over the course of a run. Exact visualization and scale limits should be verified in official docs and tested with your run volume.<\/p>\n<\/li>\n<li>\n<p><strong>How do I use it with Vertex AI Pipelines?<\/strong><br\/>\n   Typically, you initialize experiment context in pipeline components and log run parameters\/metrics as part of component execution. Confirm current recommended patterns in official Vertex AI Pipelines + Experiments samples.<\/p>\n<\/li>\n<li>\n<p><strong>Is the service regional?<\/strong><br\/>\n   Vertex AI resources are generally regional. Use a consistent location (e.g., <code>us-central1<\/code>) across your workflow to avoid confusion.<\/p>\n<\/li>\n<li>\n<p><strong>What IAM role do I need to log experiments?<\/strong><br\/>\n   Commonly <code>roles\/aiplatform.user<\/code> is sufficient for many workflows. Exact least-privilege requirements can vary; verify with IAM documentation and your org policies.<\/p>\n<\/li>\n<li>\n<p><strong>Can I use service accounts for logging?<\/strong><br\/>\n   Yes\u2014and you should for automation (pipelines\/CI). Make sure the service account has the required Vertex AI permissions.<\/p>\n<\/li>\n<li>\n<p><strong>Will experiment tracking add a lot of cost?<\/strong><br\/>\n   Usually the major costs are training\/pipelines\/storage, not the metadata logs. But high-volume logging, long retention, TensorBoard usage, and artifact storage can increase costs. Always review billing reports.<\/p>\n<\/li>\n<li>\n<p><strong>What\u2019s a good naming convention for runs?<\/strong><br\/>\n   Use a timestamp and a code reference: <code>20260414-1530-commitabc123<\/code>. Add a short descriptor if helpful: <code>20260414-1530-abc123-lr005<\/code>.<\/p>\n<\/li>\n<li>\n<p><strong>Can I export experiment data to BigQuery?<\/strong><br\/>\n   Export patterns may exist via SDK\/API queries and writing results to BigQuery yourself. Verify current APIs and supported export capabilities in official docs.<\/p>\n<\/li>\n<li>\n<p><strong>What happens if my training script crashes mid-run?<\/strong><br\/>\n   The run may not be properly finalized. Use <code>try\/finally<\/code> to call <code>end_run()<\/code> and log failure status as a parameter\/metric if your process supports it.<\/p>\n<\/li>\n<\/ol>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">17. Top Online Resources to Learn Vertex AI Experiments<\/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 Experiments overview<\/td>\n<td>Canonical feature description, concepts, and workflows. https:\/\/cloud.google.com\/vertex-ai\/docs\/experiments\/intro<\/td>\n<\/tr>\n<tr>\n<td>Official SDK reference<\/td>\n<td>Vertex AI Python SDK (<code>google-cloud-aiplatform<\/code>)<\/td>\n<td>Shows current classes\/methods for experiments and runs. https:\/\/cloud.google.com\/python\/docs\/reference\/aiplatform\/latest<\/td>\n<\/tr>\n<tr>\n<td>Official pricing page<\/td>\n<td>Vertex AI pricing<\/td>\n<td>Understand cost drivers across training, pipelines, and related services. https:\/\/cloud.google.com\/vertex-ai\/pricing<\/td>\n<\/tr>\n<tr>\n<td>Official calculator<\/td>\n<td>Google Cloud Pricing Calculator<\/td>\n<td>Build estimates for training, storage, and pipeline runs. https:\/\/cloud.google.com\/products\/calculator<\/td>\n<\/tr>\n<tr>\n<td>Official locations<\/td>\n<td>Vertex AI locations<\/td>\n<td>Choose supported regions and plan architecture. https:\/\/cloud.google.com\/vertex-ai\/docs\/general\/locations<\/td>\n<\/tr>\n<tr>\n<td>Official IAM guide<\/td>\n<td>Vertex AI access control<\/td>\n<td>Configure least privilege and understand roles. https:\/\/cloud.google.com\/vertex-ai\/docs\/general\/access-control<\/td>\n<\/tr>\n<tr>\n<td>Official YouTube<\/td>\n<td>Google Cloud Tech \/ Vertex AI content<\/td>\n<td>Practical demos and updates (search within official channel). https:\/\/www.youtube.com\/@googlecloudtech<\/td>\n<\/tr>\n<tr>\n<td>Official samples (GitHub)<\/td>\n<td>GoogleCloudPlatform samples (Vertex AI)<\/td>\n<td>Reference implementations for Vertex AI workflows (verify current experiment-related samples). https:\/\/github.com\/GoogleCloudPlatform<\/td>\n<\/tr>\n<tr>\n<td>Hands-on labs<\/td>\n<td>Google Cloud Skills Boost (Vertex AI)<\/td>\n<td>Guided labs for Vertex AI fundamentals; supplement with experiment tracking patterns. https:\/\/www.cloudskillsboost.google\/<\/td>\n<\/tr>\n<\/tbody>\n<\/table><\/figure>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">18. Training and Certification Providers<\/h2>\n\n\n\n<figure class=\"wp-block-table\"><table>\n<thead>\n<tr>\n<th>Institute<\/th>\n<th>Suitable Audience<\/th>\n<th>Likely Learning Focus<\/th>\n<th>Mode<\/th>\n<th>Website URL<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>DevOpsSchool.com<\/td>\n<td>DevOps engineers, SREs, platform teams, cloud engineers<\/td>\n<td>DevOps\/MLOps foundations, CI\/CD, cloud operations; may include Google Cloud integrations<\/td>\n<td>Check website<\/td>\n<td>https:\/\/www.devopsschool.com\/<\/td>\n<\/tr>\n<tr>\n<td>ScmGalaxy.com<\/td>\n<td>Beginners to intermediate engineers<\/td>\n<td>SCM, DevOps practices, automation; may complement MLOps workflows<\/td>\n<td>Check website<\/td>\n<td>https:\/\/www.scmgalaxy.com\/<\/td>\n<\/tr>\n<tr>\n<td>CLoudOpsNow.in<\/td>\n<td>Cloud operations and platform teams<\/td>\n<td>Cloud operations, automation, governance; may include Google Cloud operational practices<\/td>\n<td>Check website<\/td>\n<td>https:\/\/cloudopsnow.in\/<\/td>\n<\/tr>\n<tr>\n<td>SreSchool.com<\/td>\n<td>SREs and operations teams<\/td>\n<td>Reliability engineering practices for cloud workloads<\/td>\n<td>Check website<\/td>\n<td>https:\/\/sreschool.com\/<\/td>\n<\/tr>\n<tr>\n<td>AiOpsSchool.com<\/td>\n<td>Engineers and architects adopting AIOps<\/td>\n<td>AIOps concepts, monitoring, automation; may complement ML platform operations<\/td>\n<td>Check website<\/td>\n<td>https:\/\/aiopsschool.com\/<\/td>\n<\/tr>\n<\/tbody>\n<\/table><\/figure>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">19. Top Trainers<\/h2>\n\n\n\n<figure class=\"wp-block-table\"><table>\n<thead>\n<tr>\n<th>Platform\/Site<\/th>\n<th>Likely Specialization<\/th>\n<th>Suitable Audience<\/th>\n<th>Website URL<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>RajeshKumar.xyz<\/td>\n<td>DevOps\/cloud training content (verify specific offerings)<\/td>\n<td>Beginners to practitioners looking for structured training<\/td>\n<td>https:\/\/rajeshkumar.xyz\/<\/td>\n<\/tr>\n<tr>\n<td>devopstrainer.in<\/td>\n<td>DevOps training platform (verify course catalog)<\/td>\n<td>DevOps engineers, SREs, platform teams<\/td>\n<td>https:\/\/www.devopstrainer.in\/<\/td>\n<\/tr>\n<tr>\n<td>devopsfreelancer.com<\/td>\n<td>Freelance DevOps services\/training platform (treat as a resource directory unless verified)<\/td>\n<td>Teams seeking short-term help or mentorship<\/td>\n<td>https:\/\/www.devopsfreelancer.com\/<\/td>\n<\/tr>\n<tr>\n<td>devopssupport.in<\/td>\n<td>DevOps support and training-style resources (verify offerings)<\/td>\n<td>Ops teams needing hands-on support<\/td>\n<td>https:\/\/www.devopssupport.in\/<\/td>\n<\/tr>\n<\/tbody>\n<\/table><\/figure>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">20. Top Consulting Companies<\/h2>\n\n\n\n<figure class=\"wp-block-table\"><table>\n<thead>\n<tr>\n<th>Company<\/th>\n<th>Likely Service Area<\/th>\n<th>Where They May Help<\/th>\n<th>Consulting Use Case Examples<\/th>\n<th>Website URL<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>cotocus.com<\/td>\n<td>Cloud\/DevOps consulting (verify specific practice areas)<\/td>\n<td>Architecture, automation, platform improvements<\/td>\n<td>Designing CI\/CD for ML workflows; building standardized experiment logging templates<\/td>\n<td>https:\/\/cotocus.com\/<\/td>\n<\/tr>\n<tr>\n<td>DevOpsSchool.com<\/td>\n<td>DevOps consulting and training services<\/td>\n<td>DevOps\/MLOps process and tooling enablement<\/td>\n<td>Establishing MLOps pipeline patterns; governance and operational readiness reviews<\/td>\n<td>https:\/\/www.devopsschool.com\/<\/td>\n<\/tr>\n<tr>\n<td>DEVOPSCONSULTING.IN<\/td>\n<td>DevOps consulting (verify service scope)<\/td>\n<td>DevOps transformations and cloud operations<\/td>\n<td>Implementing secure service accounts and least-privilege IAM for ML pipelines; reliability improvements<\/td>\n<td>https:\/\/devopsconsulting.in\/<\/td>\n<\/tr>\n<\/tbody>\n<\/table><\/figure>\n\n\n\n<hr class=\"wp-block-separator\" \/>\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 Experiments<\/h3>\n\n\n\n<p>To use Vertex AI Experiments effectively, you should be comfortable with:\n&#8211; Google Cloud fundamentals:\n  &#8211; projects, billing, IAM\n  &#8211; regions and quotas\n&#8211; Basic ML workflow:\n  &#8211; training vs evaluation\n  &#8211; overfitting and validation\n  &#8211; metrics selection and dataset splits\n&#8211; Python basics and environment management:\n  &#8211; <code>venv<\/code>, dependencies, reproducible requirements<\/p>\n\n\n\n<p>Recommended Google Cloud learning prerequisites:\n&#8211; IAM basics and least privilege\n&#8211; Cloud Storage and BigQuery basics\n&#8211; Vertex AI fundamentals (Workbench, Training, Model Registry)<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">What to learn after Vertex AI Experiments<\/h3>\n\n\n\n<p>To operationalize and scale:\n&#8211; Vertex AI Pipelines (production orchestration)\n&#8211; Model Registry + deployment patterns (endpoints, batch prediction)\n&#8211; CI\/CD for ML (Cloud Build, Artifact Registry)\n&#8211; Observability:\n  &#8211; Cloud Logging\/Monitoring\n  &#8211; Model monitoring where applicable (verify current Vertex AI monitoring features for your model types)\n&#8211; Security:\n  &#8211; service accounts, workload identity (where applicable)\n  &#8211; VPC Service Controls patterns for AI workloads<\/p>\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>Data Scientist (experiment tracking, reproducibility)<\/li>\n<li>ML Engineer (pipeline-integrated experiment tracking)<\/li>\n<li>MLOps Engineer \/ Platform Engineer (standards, templates, governance)<\/li>\n<li>Cloud Architect (end-to-end ML architecture and controls)<\/li>\n<li>SRE\/Operations Engineer (reliability and cost management of ML platforms)<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Certification path (if available)<\/h3>\n\n\n\n<p>Vertex AI Experiments is part of broader Google Cloud AI and ML skills rather than a standalone certification topic. Consider Google Cloud certifications that cover ML\/Vertex AI (verify current certification names and outlines):\n&#8211; https:\/\/cloud.google.com\/learn\/certification<\/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 \u201cbaseline vs improved\u201d classification experiment:<\/li>\n<li>Run 10 variants with different feature sets and log results.<\/li>\n<li>Create a minimal CI pipeline:<\/li>\n<li>On PR, run evaluation, log an experiment run, and post summary.<\/li>\n<li>Build a pipeline that:<\/li>\n<li>trains \u2192 evaluates \u2192 logs metrics \u2192 registers best model<\/li>\n<li>Establish an \u201cexperiment schema\u201d:<\/li>\n<li>enforce required params (<code>dataset_version<\/code>, <code>commit_sha<\/code>, <code>owner<\/code>) in code.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">22. Glossary<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Vertex AI Experiments<\/strong>: Vertex AI feature for tracking experiments and runs with parameters and metrics.<\/li>\n<li><strong>Experiment<\/strong>: A logical container grouping multiple related runs.<\/li>\n<li><strong>Run<\/strong>: A single trial\/execution within an experiment; holds logged parameters and metrics.<\/li>\n<li><strong>Parameter<\/strong>: Input configuration for a run (hyperparameters, dataset version, seed).<\/li>\n<li><strong>Metric<\/strong>: Measured outcome from a run (AUC, loss, accuracy, RMSE).<\/li>\n<li><strong>Reproducibility<\/strong>: Ability to recreate results using the same code, data, and configuration.<\/li>\n<li><strong>IAM (Identity and Access Management)<\/strong>: Google Cloud\u2019s access control system for permissions.<\/li>\n<li><strong>ADC (Application Default Credentials)<\/strong>: Standard method for Google Cloud authentication in many environments.<\/li>\n<li><strong>Vertex AI Workbench<\/strong>: Managed notebook environment for ML development on Google Cloud.<\/li>\n<li><strong>Vertex AI Pipelines<\/strong>: Managed orchestration for ML workflows.<\/li>\n<li><strong>Model Registry<\/strong>: Central place to version and manage models in Vertex AI.<\/li>\n<li><strong>Artifact<\/strong>: Output files like model binaries, evaluation reports, and logs (often stored in Cloud Storage).<\/li>\n<li><strong>Cloud Audit Logs<\/strong>: Records of administrative and data access activities for supported Google Cloud services.<\/li>\n<li><strong>CMEK<\/strong>: Customer-Managed Encryption Keys (KMS-managed keys you control) for supported services.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">23. Summary<\/h2>\n\n\n\n<p>Vertex AI Experiments (Google Cloud, AI and ML) is Vertex AI\u2019s experiment tracking capability for logging and comparing ML runs with consistent parameters, metrics, and related metadata. It matters because experiment sprawl is one of the biggest practical blockers to reproducibility, collaboration, and safe model promotion\u2014especially as teams move from notebooks to pipelines and production MLOps.<\/p>\n\n\n\n<p>Cost-wise, experiment tracking is usually not the primary driver; the real spend is in training\/pipelines, artifact storage, logging volume, and (optionally) TensorBoard. Security-wise, it aligns naturally with Google Cloud IAM and audit logging, but you must still avoid logging sensitive data and enforce least privilege with service accounts.<\/p>\n\n\n\n<p>Use Vertex AI Experiments when you want managed experiment tracking tightly integrated with Vertex AI workflows. Next, deepen your implementation by standardizing run metadata (dataset and code versioning) and integrating experiment logging into Vertex AI Pipelines and CI\/CD so experiment tracking becomes automatic rather than manual.<\/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-566","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\/566","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=566"}],"version-history":[{"count":0,"href":"https:\/\/www.devopsschool.com\/tutorials\/wp-json\/wp\/v2\/posts\/566\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.devopsschool.com\/tutorials\/wp-json\/wp\/v2\/media?parent=566"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.devopsschool.com\/tutorials\/wp-json\/wp\/v2\/categories?post=566"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.devopsschool.com\/tutorials\/wp-json\/wp\/v2\/tags?post=566"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}