{"id":565,"date":"2026-04-14T13:06:06","date_gmt":"2026-04-14T13:06:06","guid":{"rendered":"https:\/\/www.devopsschool.com\/tutorials\/google-cloud-vertex-ai-datasets-tutorial-architecture-pricing-use-cases-and-hands-on-guide-for-ai-and-ml\/"},"modified":"2026-04-14T13:06:06","modified_gmt":"2026-04-14T13:06:06","slug":"google-cloud-vertex-ai-datasets-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-datasets-tutorial-architecture-pricing-use-cases-and-hands-on-guide-for-ai-and-ml\/","title":{"rendered":"Google Cloud Vertex AI Datasets 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 Datasets is the dataset management capability inside <strong>Vertex AI<\/strong> on <strong>Google Cloud<\/strong>. It lets you register, organize, and reuse training\/evaluation data for ML workflows (AutoML and custom training) in a consistent way\u2014without everyone on the team manually tracking \u201cwhich bucket\/path\/table did we train on?\u201d<\/p>\n\n\n\n<p>In simple terms: <strong>Vertex AI Datasets is a catalog of ML-ready datasets<\/strong> (tabular, image, text, video) that points to your source data (typically <strong>Cloud Storage<\/strong> or <strong>BigQuery<\/strong>) and can hold labeling\/annotation metadata. It provides a standard entry point for downstream ML tasks like training, evaluation, and labeling jobs.<\/p>\n\n\n\n<p>Technically: a Vertex AI Dataset is a <strong>regional Vertex AI resource<\/strong> (<code>projects\/*\/locations\/*\/datasets\/*<\/code>) that stores dataset metadata (display name, schema, data item references, labels\/annotations) and links to underlying data sources. You can import data from Cloud Storage and\/or BigQuery (depending on dataset type), and then use the dataset as the input to Vertex AI training pipelines (AutoML or custom) and data labeling workflows.<\/p>\n\n\n\n<p>The problem it solves: ML teams often struggle with dataset sprawl\u2014many versions of CSVs, folders, and tables with unclear lineage. Vertex AI Datasets provides a <strong>structured dataset object<\/strong> that makes it easier to:\n&#8211; collaborate across data\/ML\/ops teams,\n&#8211; standardize training inputs,\n&#8211; apply consistent access controls,\n&#8211; reduce mistakes (training on the wrong snapshot\/path),\n&#8211; operationalize dataset-driven MLOps workflows.<\/p>\n\n\n\n<blockquote>\n<p>Naming note (verify if your org uses legacy terms): Vertex AI is the successor to \u201cAI Platform.\u201d Dataset management is now part of Vertex AI and is commonly referred to as <strong>Vertex AI Datasets<\/strong> in docs and console.<\/p>\n<\/blockquote>\n\n\n\n<h2 class=\"wp-block-heading\">2. What is Vertex AI Datasets?<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Official purpose<\/h3>\n\n\n\n<p>Vertex AI Datasets is the <strong>Vertex AI data management layer<\/strong> for creating and managing dataset resources used in ML workflows. It is designed to help teams prepare and manage data for training, evaluation, and labeling inside the Vertex AI ecosystem.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Core capabilities<\/h3>\n\n\n\n<p>At a practical level, Vertex AI Datasets enables you to:\n&#8211; <strong>Create datasets<\/strong> for supported data types (commonly tabular, image, text, and video).\n&#8211; <strong>Import data<\/strong> from supported sources (commonly <strong>BigQuery<\/strong> for tabular; <strong>Cloud Storage<\/strong> for media\/text).\n&#8211; <strong>Manage labels\/annotations<\/strong> (often via Vertex AI Data Labeling integration).\n&#8211; <strong>Reuse datasets<\/strong> across experiments, training jobs, and pipelines.\n&#8211; <strong>Control access<\/strong> using Google Cloud IAM.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Major components (conceptual)<\/h3>\n\n\n\n<p>While the exact objects vary by dataset type, common concepts include:\n&#8211; <strong>Dataset resource<\/strong>: the top-level container in Vertex AI (regional).\n&#8211; <strong>Data items<\/strong>: references to individual records (rows, files, documents, frames\/clips).\n&#8211; <strong>Annotations\/labels<\/strong>: metadata created by labeling jobs or imported labels.\n&#8211; <strong>Schema<\/strong>: metadata schema describing the dataset type and expected fields.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Service type<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Managed service<\/strong> within <strong>Vertex AI<\/strong> (control plane managed by Google).<\/li>\n<li>You interact with it via:<\/li>\n<li>Google Cloud Console (Vertex AI \u2192 Datasets)<\/li>\n<li>Vertex AI API (<code>aiplatform.googleapis.com<\/code>)<\/li>\n<li><code>gcloud<\/code> CLI (<code>gcloud ai datasets ...<\/code>)<\/li>\n<li>Vertex AI SDKs (Python commonly)<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Scope: regional and project-scoped<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Project-scoped<\/strong>: datasets live inside a Google Cloud project.<\/li>\n<li><strong>Regional<\/strong>: datasets are created in a specific Vertex AI <strong>location<\/strong> (for example, <code>us-central1<\/code>, <code>europe-west4<\/code>, etc.).<br\/>\n  Resource name format resembles:<\/li>\n<li><code>projects\/PROJECT_ID\/locations\/LOCATION\/datasets\/DATASET_ID<\/code><\/li>\n<\/ul>\n\n\n\n<p><strong>Important<\/strong>: Even if your dataset data lives in Cloud Storage or BigQuery, the <em>Vertex AI dataset resource<\/em> is regional. For data residency, performance, and compliance, align:\n&#8211; Vertex AI dataset location\n&#8211; underlying storage locations (BigQuery dataset location; Cloud Storage bucket location)<\/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 Datasets is typically used alongside:\n&#8211; <strong>Cloud Storage<\/strong>: raw files and media assets\n&#8211; <strong>BigQuery<\/strong>: tabular datasets and analytics\n&#8211; <strong>Vertex AI Training \/ AutoML<\/strong>: training jobs that consume datasets\n&#8211; <strong>Vertex AI Pipelines<\/strong>: orchestrating repeatable ML workflows\n&#8211; <strong>Vertex AI Data Labeling<\/strong>: human labeling\/annotation operations\n&#8211; <strong>IAM + Cloud Audit Logs<\/strong>: access control and auditing\n&#8211; <strong>Dataplex \/ Data Catalog<\/strong> (governance): governing the underlying storage and metadata (Vertex AI Datasets is not a full governance suite by itself)<\/p>\n\n\n\n<p>Official docs starting point:<br\/>\n&#8211; https:\/\/cloud.google.com\/vertex-ai\/docs\/datasets\/introduction<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">3. Why use Vertex AI Datasets?<\/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>: dataset resources become reusable building blocks for training workflows.<\/li>\n<li><strong>Reduced risk<\/strong>: fewer \u201ctrained on the wrong file\/table\u201d incidents because datasets are tracked and referenced consistently.<\/li>\n<li><strong>Better collaboration<\/strong>: a shared dataset registry is easier than passing around paths and ad-hoc spreadsheets.<\/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 ML inputs<\/strong>: downstream Vertex AI services can consume dataset IDs rather than fragile storage paths.<\/li>\n<li><strong>Support for multiple data modalities<\/strong>: separate dataset types for tabular, image, text, and video (verify supported types for your region and workflow in official docs).<\/li>\n<li><strong>Labeling integration<\/strong>: labeling workflows can attach annotations to the dataset resource.<\/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>Repeatability<\/strong>: stable dataset resources fit better into CI\/CD and MLOps patterns.<\/li>\n<li><strong>Central visibility<\/strong>: teams can discover datasets via console\/API and inspect schema\/metadata.<\/li>\n<li><strong>Lifecycle management<\/strong>: you can delete datasets, rotate permissions, and standardize naming conventions.<\/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 access control<\/strong>: control who can view\/manage datasets and who can access underlying data sources.<\/li>\n<li><strong>Auditability<\/strong>: dataset actions are logged via Cloud Audit Logs (subject to your org\u2019s logging configuration).<\/li>\n<li><strong>Data residency alignment<\/strong>: choose dataset locations aligned to regulatory needs and storage locations.<\/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>Decouples control plane from data plane<\/strong>: the dataset resource is metadata, while the heavy data stays in BigQuery\/Cloud Storage.<\/li>\n<li><strong>Works with large sources<\/strong>: BigQuery tables and Cloud Storage buckets scale independently.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">When teams should choose Vertex AI Datasets<\/h3>\n\n\n\n<p>Choose Vertex AI Datasets when:\n&#8211; You are standardizing ML workflows on <strong>Vertex AI<\/strong>.\n&#8211; Multiple people\/teams share training data and need consistent references and permissions.\n&#8211; You want to integrate labeling, AutoML, training pipelines, and model registry around consistent dataset assets.\n&#8211; You need a managed dataset registry without building your own dataset metadata service.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">When teams should not choose Vertex AI Datasets<\/h3>\n\n\n\n<p>You might skip Vertex AI Datasets if:\n&#8211; You are not using Vertex AI for training or MLOps (a dataset registry may not add value).\n&#8211; Your workflow is fully external (for example, training entirely on-prem) and you only use Google Cloud for storage.\n&#8211; You require advanced dataset versioning\/branching semantics (Git-like) and governance features\u2014consider complementary tools (DVC, lakeFS, Dataplex) and integrate as needed.\n&#8211; Your primary need is enterprise data governance and cataloging; Vertex AI Datasets is not a replacement for a data governance platform.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">4. Where is Vertex AI Datasets 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\/e-commerce: product categorization, demand forecasting, personalization datasets<\/li>\n<li>Financial services: fraud and risk tabular datasets, document\/text classification<\/li>\n<li>Healthcare\/life sciences: imaging datasets (subject to compliance controls), NLP datasets<\/li>\n<li>Manufacturing: quality inspection image\/video datasets<\/li>\n<li>Media\/advertising: content classification and moderation datasets<\/li>\n<li>Transportation\/logistics: ETA prediction, route optimization tabular data<\/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 building models and experiments<\/li>\n<li>ML engineering teams operationalizing training pipelines<\/li>\n<li>Platform teams standardizing Vertex AI usage<\/li>\n<li>Security and governance teams enforcing IAM and audit controls<\/li>\n<li>Data engineering teams managing upstream BigQuery\/Storage sources<\/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>Supervised learning with labels\/annotations<\/li>\n<li>Computer vision: classification, object detection (verify exact supported annotation formats per dataset type)<\/li>\n<li>NLP: classification, entity extraction (verify supported dataset types and formats)<\/li>\n<li>Tabular classification\/regression<\/li>\n<li>Video classification\/object tracking (verify supported capabilities)<\/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>Cloud-native MLOps (Vertex AI Pipelines + datasets + training + registry)<\/li>\n<li>BigQuery-centric ML where data stays in BigQuery and Vertex AI consumes it<\/li>\n<li>Data lake on Cloud Storage feeding labeled training datasets<\/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><strong>Production<\/strong>: curated datasets feeding repeatable training pipelines, controlled by IAM and CI\/CD<\/li>\n<li><strong>Dev\/test<\/strong>: smaller sandbox datasets used for experimentation, model prototyping, and pipeline validation<\/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 Datasets fits well.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">1) Tabular churn prediction dataset registry<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Problem<\/strong>: Analysts create many versions of churn tables in BigQuery; ML engineers lose track of which table was used for training.<\/li>\n<li><strong>Why Vertex AI Datasets fits<\/strong>: A tabular dataset resource can reference the canonical BigQuery table and become the stable input to training pipelines.<\/li>\n<li><strong>Example<\/strong>: Create <code>customer_churn_tabular<\/code> dataset in <code>us-central1<\/code> referencing <code>bq:\/\/project.ds.churn_features_v3<\/code>.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">2) Image classification for product categories<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Problem<\/strong>: Product images stored in Cloud Storage are not consistently labeled; training data is scattered across folders.<\/li>\n<li><strong>Why it fits<\/strong>: Vertex AI Datasets organizes images as data items with labels\/annotations, and integrates with labeling.<\/li>\n<li><strong>Example<\/strong>: A retail team imports images from <code>gs:\/\/...\/products\/<\/code> and assigns category labels for AutoML training.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">3) Defect detection via object detection labels<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Problem<\/strong>: Manufacturing needs bounding boxes for defects across many assembly-line photos.<\/li>\n<li><strong>Why it fits<\/strong>: Image datasets can hold object detection annotations (verify the supported import\/annotation formats for your workflow).<\/li>\n<li><strong>Example<\/strong>: Labelers annotate defects; training pipeline consumes the dataset for detection model training.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">4) Document\/text classification for support ticket routing<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Problem<\/strong>: Support tickets in text form require labeling by category\/priority; labels need to be reused for retraining.<\/li>\n<li><strong>Why it fits<\/strong>: Text datasets help centralize labeled text samples and feed supervised training.<\/li>\n<li><strong>Example<\/strong>: Import ticket text from Cloud Storage, label intents, and reuse the dataset for monthly retraining.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">5) Sentiment analysis dataset across regions<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Problem<\/strong>: Regional teams store training text in different buckets; compliance requires data locality.<\/li>\n<li><strong>Why it fits<\/strong>: Datasets are regional resources; you can create region-specific datasets aligned to storage.<\/li>\n<li><strong>Example<\/strong>: <code>sentiment-eu<\/code> in <code>europe-west4<\/code> referencing EU storage; separate dataset <code>sentiment-us<\/code> in <code>us-central1<\/code>.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">6) Video dataset for content moderation<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Problem<\/strong>: Moderation needs labeled video clips and consistent training splits.<\/li>\n<li><strong>Why it fits<\/strong>: Video datasets can organize video data items and annotations (verify supported formats and labeling tasks).<\/li>\n<li><strong>Example<\/strong>: Import clips from Cloud Storage, label unsafe content categories, train classifier.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">7) Central dataset catalog for an MLOps platform team<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Problem<\/strong>: Each squad builds its own dataset conventions; onboarding is slow.<\/li>\n<li><strong>Why it fits<\/strong>: Platform team defines standards: naming, IAM groups, and dataset locations.<\/li>\n<li><strong>Example<\/strong>: A \u201cdataset registry\u201d per domain: <code>fraud_*<\/code>, <code>search_*<\/code>, <code>vision_*<\/code>.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">8) Reproducible training input for Vertex AI Pipelines<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Problem<\/strong>: Pipelines reference raw paths; refactors break training jobs.<\/li>\n<li><strong>Why it fits<\/strong>: Pipelines can reference dataset IDs, reducing fragile path dependencies.<\/li>\n<li><strong>Example<\/strong>: Pipeline step fetches dataset resource and triggers training with the dataset as input.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">9) Controlled external labeling with auditability<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Problem<\/strong>: Need to let a labeling vendor annotate data without broad bucket access.<\/li>\n<li><strong>Why it fits<\/strong>: With careful IAM and storage permissions, you can limit access and audit operations (design carefully; verify best practices in official docs).<\/li>\n<li><strong>Example<\/strong>: Vendor gets minimal permissions; dataset annotation changes are auditable.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">10) Multi-model training from a shared \u201cgolden dataset\u201d<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Problem<\/strong>: Multiple models (baseline, advanced, interpretable) should train on the same curated dataset.<\/li>\n<li><strong>Why it fits<\/strong>: A single dataset resource becomes the canonical input; different training jobs reuse it.<\/li>\n<li><strong>Example<\/strong>: Train baseline logistic regression and more complex models from the same dataset asset.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">6. Core Features<\/h2>\n\n\n\n<blockquote>\n<p>Feature availability and exact dataset type support can change by region and over time. Verify in official docs if you rely on a specific dataset type, annotation format, or import path.<\/p>\n<\/blockquote>\n\n\n\n<h3 class=\"wp-block-heading\">1) Dataset resources for multiple data modalities<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>What it does<\/strong>: Lets you create datasets for different ML modalities (commonly tabular, image, text, video).<\/li>\n<li><strong>Why it matters<\/strong>: ML workflows differ by modality; schema and import formats vary.<\/li>\n<li><strong>Practical benefit<\/strong>: Teams can standardize dataset creation per modality and use consistent tooling.<\/li>\n<li><strong>Caveats<\/strong>: Not all dataset types and labeling tasks are available in all regions. Verify supported locations and dataset types in Vertex AI docs.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">2) Import from Cloud Storage and\/or BigQuery (depending on dataset type)<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>What it does<\/strong>: Creates dataset data items by importing references from GCS URIs or BigQuery tables.<\/li>\n<li><strong>Why it matters<\/strong>: Keeps your data in scalable systems (GCS\/BQ) while enabling ML workflows in Vertex AI.<\/li>\n<li><strong>Practical benefit<\/strong>: Avoids ad-hoc local file management; supports larger datasets.<\/li>\n<li><strong>Caveats<\/strong>: Location mismatches (Vertex AI region vs bucket\/BQ dataset location) can cause friction or performance issues. Align locations where possible.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">3) Labeling and annotation integration<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>What it does<\/strong>: Supports attaching labels\/annotations to dataset items (often via Vertex AI Data Labeling workflows).<\/li>\n<li><strong>Why it matters<\/strong>: Supervised learning depends on high-quality labels.<\/li>\n<li><strong>Practical benefit<\/strong>: Central place to store labeling output tied to data items.<\/li>\n<li><strong>Caveats<\/strong>: Labeling incurs cost and requires careful IAM design. Some labeling workflows have task-specific formats and constraints.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">4) Dataset metadata and organization<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>What it does<\/strong>: Provides display names, resource labels\/tags (where supported), schemas, and dataset-level metadata.<\/li>\n<li><strong>Why it matters<\/strong>: Discoverability and governance.<\/li>\n<li><strong>Practical benefit<\/strong>: Standard naming conventions and labels help manage many datasets across teams.<\/li>\n<li><strong>Caveats<\/strong>: Vertex AI Datasets is not a full enterprise data catalog; rely on Dataplex\/Data Catalog for broader governance.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">5) API\/SDK\/CLI management<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>What it does<\/strong>: Create\/list\/describe\/delete datasets programmatically.<\/li>\n<li><strong>Why it matters<\/strong>: Enables automation and MLOps.<\/li>\n<li><strong>Practical benefit<\/strong>: Integrate dataset creation into CI\/CD or environment bootstrapping.<\/li>\n<li><strong>Caveats<\/strong>: Quotas and permissions apply; ensure least privilege.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">6) Integration with Vertex AI training workflows<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>What it does<\/strong>: Many Vertex AI training flows (including AutoML for supported modalities) can consume a dataset resource.<\/li>\n<li><strong>Why it matters<\/strong>: Reduces glue code and makes training inputs consistent.<\/li>\n<li><strong>Practical benefit<\/strong>: Easier reproducibility when training jobs reference a dataset ID.<\/li>\n<li><strong>Caveats<\/strong>: Some custom training workflows may still read directly from GCS\/BQ; dataset resources are helpful but not always required.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">7) Regional resource control<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>What it does<\/strong>: Dataset resources are created in a chosen Vertex AI region.<\/li>\n<li><strong>Why it matters<\/strong>: Data residency, latency, and compliance.<\/li>\n<li><strong>Practical benefit<\/strong>: Align datasets to regulated regions and keep workflows consistent.<\/li>\n<li><strong>Caveats<\/strong>: Moving a dataset between regions is not typically a \u201cmove\u201d operation; you often recreate\/import in the target region.<\/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 architecture<\/h3>\n\n\n\n<p>Vertex AI Datasets separates <strong>dataset metadata management<\/strong> (Vertex AI control plane) from <strong>data storage<\/strong> (Cloud Storage\/BigQuery). The dataset resource:\n&#8211; stores schema and dataset metadata,\n&#8211; stores references to the underlying data items (file URIs, table references),\n&#8211; stores labeling\/annotation metadata (depending on dataset type and workflow),\n&#8211; is used by downstream Vertex AI services for training and labeling.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Request\/data\/control flow (typical)<\/h3>\n\n\n\n<ol class=\"wp-block-list\">\n<li>You create a dataset in a Vertex AI region.<\/li>\n<li>You run an import (via console, API, SDK, or CLI).<\/li>\n<li>Vertex AI records dataset items and metadata, referencing your data in GCS or BigQuery.<\/li>\n<li>You optionally run labeling jobs and attach annotations to dataset items.<\/li>\n<li>Training jobs consume the dataset resource (or underlying sources), producing models and artifacts.<\/li>\n<\/ol>\n\n\n\n<h3 class=\"wp-block-heading\">Integrations with related services<\/h3>\n\n\n\n<p>Common integrations include:\n&#8211; <strong>Cloud Storage<\/strong>: file-based sources for image\/video\/text.\n&#8211; <strong>BigQuery<\/strong>: tabular sources and feature tables.\n&#8211; <strong>Vertex AI Training \/ AutoML<\/strong>: training consumes dataset resources.\n&#8211; <strong>Vertex AI Pipelines<\/strong>: orchestrates recurring dataset import + training.\n&#8211; <strong>Cloud Logging \/ Cloud Monitoring<\/strong>: operational observability for API calls and jobs.\n&#8211; <strong>IAM \/ Cloud Audit Logs<\/strong>: access control and auditing.\n&#8211; <strong>Dataplex \/ Data Catalog<\/strong>: governance of underlying data stores (complementary).<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Dependency services<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><code>aiplatform.googleapis.com<\/code> (Vertex AI API)<\/li>\n<li>BigQuery API (if using BigQuery sources)<\/li>\n<li>Cloud Storage API (if using GCS sources)<\/li>\n<li>IAM and Service Usage for API enablement<\/li>\n<li>Cloud Logging\/Audit Logs (for monitoring\/auditing)<\/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>Uses <strong>Google Cloud IAM<\/strong> for dataset resource access.<\/li>\n<li>Uses <strong>service accounts<\/strong> for programmatic access (SDK\/CLI).<\/li>\n<li>Underlying data access is enforced by the data plane service:<\/li>\n<li>BigQuery IAM for tables<\/li>\n<li>Cloud Storage IAM for buckets\/objects<\/li>\n<\/ul>\n\n\n\n<p>A common pitfall is granting Vertex AI dataset permissions without granting access to the referenced BigQuery table or GCS objects (or vice versa). You need both.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Networking model<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Vertex AI is a managed Google Cloud service accessed via Google APIs.<\/li>\n<li>Most usage is over public Google API endpoints, secured by IAM and TLS.<\/li>\n<li>Enterprises often restrict access using:<\/li>\n<li><strong>Private Google Access<\/strong> (for VMs in VPC accessing Google APIs without external IPs)<\/li>\n<li><strong>VPC Service Controls<\/strong> (service perimeter around Vertex AI, BigQuery, Storage)<br\/>\n  Verify the latest Vertex AI + VPC SC guidance in official docs.<\/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>: dataset create\/delete\/import operations are typically auditable.<\/li>\n<li><strong>Cloud Logging<\/strong>: job logs (for import\/labeling) can appear depending on the operation.<\/li>\n<li><strong>Resource labels<\/strong>: use consistent labels for ownership, environment, cost center.<\/li>\n<li><strong>Data governance<\/strong>: govern underlying BigQuery\/Storage with Dataplex, IAM conditions, bucket policies, retention, and DLP as required.<\/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  U[User \/ CI Pipeline] --&gt;|Console \/ API \/ SDK| VAI[Vertex AI Datasets (regional)]\n  VAI --&gt;|References| GCS[Cloud Storage bucket]\n  VAI --&gt;|References| BQ[BigQuery table]\n  VAI --&gt;|Dataset ID| TR[Vertex AI Training \/ AutoML]\n  TR --&gt; M[Model artifacts]\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 Org[Organization]\n    IAM[IAM + Groups]\n    AL[Cloud Audit Logs]\n    VPCSC[VPC Service Controls (optional)]\n  end\n\n  subgraph Data[Data Layer]\n    GCSRAW[(Cloud Storage - raw\/curated)]\n    BQDW[(BigQuery - feature tables)]\n    DLP[DLP\/Policy checks (optional)]\n    DPX[Dataplex\/Data Catalog (governance)]\n  end\n\n  subgraph ML[Vertex AI (Regional)]\n    DS[Vertex AI Datasets]\n    LAB[Vertex AI Data Labeling (optional)]\n    PIPE[Vertex AI Pipelines]\n    TRAIN[Vertex AI Training \/ AutoML]\n    REG[Model Registry (Vertex AI)]\n  end\n\n  subgraph Ops[Operations]\n    LOG[Cloud Logging]\n    MON[Cloud Monitoring]\n    CI[CI\/CD System]\n  end\n\n  IAM --&gt; DS\n  IAM --&gt; GCSRAW\n  IAM --&gt; BQDW\n\n  DS --&gt;|imports references| GCSRAW\n  DS --&gt;|imports references| BQDW\n\n  DS --&gt; LAB\n  DS --&gt; PIPE\n  PIPE --&gt; TRAIN\n  TRAIN --&gt; REG\n\n  DS --&gt; LOG\n  PIPE --&gt; LOG\n  TRAIN --&gt; LOG\n  LOG --&gt; MON\n  DS --&gt; AL\n\n  CI --&gt;|API-driven automation| DS\n  CI --&gt; PIPE\n\n  DPX --- GCSRAW\n  DPX --- BQDW\n  DLP --- GCSRAW\n  DLP --- BQDW\n  VPCSC --- DS\n  VPCSC --- GCSRAW\n  VPCSC --- BQDW\n<\/code><\/pre>\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>Ability to enable APIs in the project.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Permissions \/ IAM roles<\/h3>\n\n\n\n<p>At minimum (principle of least privilege; adjust for your org):\n&#8211; Vertex AI:\n  &#8211; <code>roles\/aiplatform.user<\/code> for basic usage, or\n  &#8211; <code>roles\/aiplatform.admin<\/code> for full control (use sparingly)\n&#8211; BigQuery (if using BigQuery sources):\n  &#8211; <code>roles\/bigquery.dataViewer<\/code> on source tables\n  &#8211; <code>roles\/bigquery.jobUser<\/code> may be needed for some operations\n&#8211; Cloud Storage (if using GCS sources):\n  &#8211; <code>roles\/storage.objectViewer<\/code> (read)\n  &#8211; <code>roles\/storage.objectAdmin<\/code> (if uploading\/managing objects in the lab)\n&#8211; Project setup:\n  &#8211; <code>roles\/serviceusage.serviceUsageAdmin<\/code> to enable APIs (or project owner)<\/p>\n\n\n\n<p>Verify role requirements in official docs (they evolve):<br\/>\nhttps:\/\/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>Dataset metadata operations are typically low cost, but you will pay for:<\/li>\n<li>BigQuery storage\/query if used<\/li>\n<li>Cloud Storage storage\/operations if used<\/li>\n<li>Labeling jobs if used<\/li>\n<li>Any training jobs if launched<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">CLI\/SDK\/tools<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Google Cloud SDK<\/strong> (<code>gcloud<\/code>)<br\/>\n  Install: https:\/\/cloud.google.com\/sdk\/docs\/install<\/li>\n<li>Optional:<\/li>\n<li><strong>bq<\/strong> CLI (ships with Cloud SDK)<\/li>\n<li>Python 3.9+ and <code>google-cloud-aiplatform<\/code> SDK (if automating)<\/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>Choose a Vertex AI <strong>region<\/strong> supported by your organization.<\/li>\n<li>Align with data location:<\/li>\n<li>BigQuery dataset location (US\/EU or specific region)<\/li>\n<li>Cloud Storage bucket location (region\/multi-region)<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Quotas\/limits<\/h3>\n\n\n\n<p>Vertex AI enforces quotas (API request rates, resource counts, etc.). Check:<br\/>\nhttps:\/\/cloud.google.com\/vertex-ai\/quotas<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Prerequisite services to enable<\/h3>\n\n\n\n<p>In most cases:\n&#8211; Vertex AI API: <code>aiplatform.googleapis.com<\/code>\n&#8211; Cloud Storage: <code>storage.googleapis.com<\/code>\n&#8211; BigQuery: <code>bigquery.googleapis.com<\/code> (if using BigQuery sources)<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">9. Pricing \/ Cost<\/h2>\n\n\n\n<p>Vertex AI Datasets cost is best understood as <strong>(a) dataset management metadata + (b) underlying storage and jobs<\/strong>.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Official pricing sources<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Vertex AI pricing: https:\/\/cloud.google.com\/vertex-ai\/pricing  <\/li>\n<li>Google Cloud Pricing Calculator: https:\/\/cloud.google.com\/products\/calculator  <\/li>\n<li>Cloud Storage pricing: https:\/\/cloud.google.com\/storage\/pricing  <\/li>\n<li>BigQuery pricing: https:\/\/cloud.google.com\/bigquery\/pricing  <\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Pricing dimensions (what you actually pay for)<\/h3>\n\n\n\n<p>You typically pay for:\n1. <strong>Data storage<\/strong>\n   &#8211; Cloud Storage: GB stored\/month, operations (PUT\/GET\/LIST), retrieval (depending on class), replication, and potential egress.\n   &#8211; BigQuery: table storage; queries (on-demand TB processed) or capacity-based reservations.\n2. <strong>Data processing jobs<\/strong>\n   &#8211; Dataset imports may trigger data processing\/validation steps (behavior depends on dataset type). Any compute-like operations are usually priced under Vertex AI or the underlying service. <strong>Verify in official docs<\/strong> whether a specific import path triggers billable processing.\n3. <strong>Labeling<\/strong>\n   &#8211; Human labeling is billed (task type, volume, workforce).\n4. <strong>Training<\/strong>\n   &#8211; AutoML\/custom training is billed by compute, duration, and configuration.\n5. <strong>Networking<\/strong>\n   &#8211; Data egress if data crosses regions or leaves Google Cloud.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Free tier<\/h3>\n\n\n\n<p>Vertex AI has some free usage tiers for certain products, but <strong>do not assume a free tier applies to dataset operations<\/strong>. Verify current free tier details on the Vertex AI pricing page.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Cost drivers (common \u201cgotchas\u201d)<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>BigQuery query costs<\/strong> when you repeatedly transform\/export data for training.<\/li>\n<li><strong>Copying data<\/strong> into multiple buckets\/regions for convenience.<\/li>\n<li><strong>Labeling costs<\/strong> scaling with number of items and complexity.<\/li>\n<li><strong>Training costs<\/strong> triggered accidentally from the console (AutoML training can run for hours).<\/li>\n<li><strong>Storage class choices<\/strong>: using Standard vs Nearline\/Coldline; retrieval fees can surprise you if you repeatedly read cold data.<\/li>\n<\/ul>\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>Logging and monitoring ingestion (usually modest, but can grow with verbose logs).<\/li>\n<li>Inter-region data transfer if your training region differs from data region.<\/li>\n<li>CI\/CD runner costs if you automate frequent dataset imports.<\/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>Keep data and Vertex AI region aligned to reduce egress and improve performance.<\/li>\n<li>Use BigQuery views\/materialized views carefully\u2014understand query cost implications.<\/li>\n<li>Avoid duplicating full datasets for every experiment; use curated \u201cgolden\u201d datasets and track versions via tables\/snapshots.<\/li>\n<li>Use lifecycle rules on Cloud Storage buckets for raw\/intermediate data.<\/li>\n<li>For labeling, start with small pilot batches to estimate cost\/quality.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Example low-cost starter estimate (no fabricated numbers)<\/h3>\n\n\n\n<p>A minimal lab can be kept low cost by:\n&#8211; creating a small BigQuery table (KB\/MB scale),\n&#8211; creating a Vertex AI tabular dataset referencing that table,\n&#8211; avoiding training and labeling jobs.<\/p>\n\n\n\n<p>Costs will primarily be small BigQuery storage and minimal operations. Exact cost depends on region and pricing model\u2014use the Pricing Calculator for your region and expected usage.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Example production cost considerations<\/h3>\n\n\n\n<p>In production, the biggest drivers are usually:\n&#8211; large-scale data storage (TBs) in BigQuery\/Cloud Storage,\n&#8211; recurring labeling campaigns,\n&#8211; recurring training runs (AutoML or custom),\n&#8211; orchestration and compute for data prep pipelines (Dataflow\/Dataproc\/BigQuery).<\/p>\n\n\n\n<p>A good practice is to separate:\n&#8211; <strong>raw data<\/strong> (cheap, long retention),\n&#8211; <strong>curated training dataset<\/strong> (stable tables\/partitions),\n&#8211; <strong>experiment subsets<\/strong> (temporary, aggressively TTL\u2019d).<\/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 focuses on creating a real <strong>Vertex AI Datasets<\/strong> tabular dataset from a BigQuery table with minimal cost. You will:\n&#8211; create a small CSV locally,\n&#8211; load it into BigQuery,\n&#8211; create a Vertex AI Dataset that references that BigQuery table,\n&#8211; verify it exists via console and CLI,\n&#8211; clean up everything.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Objective<\/h3>\n\n\n\n<p>Create and manage a <strong>Vertex AI Datasets<\/strong> tabular dataset in Google Cloud and understand the required permissions, location alignment, verification, and cleanup steps.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Lab Overview<\/h3>\n\n\n\n<p>You will set up:\n&#8211; A Cloud Storage bucket (for staging the CSV)\n&#8211; A BigQuery dataset + table (loaded from the CSV)\n&#8211; A Vertex AI dataset (tabular) importing from the BigQuery table<\/p>\n\n\n\n<p>You will validate by:\n&#8211; viewing the dataset in Vertex AI console\n&#8211; listing\/describing the dataset using <code>gcloud<\/code><\/p>\n\n\n\n<p>You will clean up by:\n&#8211; deleting the Vertex AI dataset\n&#8211; deleting the BigQuery dataset (table)\n&#8211; deleting the Cloud Storage bucket<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Step 1: Set environment variables and enable APIs<\/h3>\n\n\n\n<p><strong>Expected outcome<\/strong>: Your project is set, APIs are enabled, and you have a chosen region.<\/p>\n\n\n\n<p>1) Authenticate and set your project:<\/p>\n\n\n\n<pre><code class=\"language-bash\">gcloud auth login\ngcloud config set project YOUR_PROJECT_ID\n<\/code><\/pre>\n\n\n\n<p>2) Choose a Vertex AI region. This example uses <code>us-central1<\/code>:<\/p>\n\n\n\n<pre><code class=\"language-bash\">export REGION=us-central1\ngcloud config set ai\/region $REGION\n<\/code><\/pre>\n\n\n\n<p>3) Enable required APIs:<\/p>\n\n\n\n<pre><code class=\"language-bash\">gcloud services enable aiplatform.googleapis.com\ngcloud services enable bigquery.googleapis.com\ngcloud services enable storage.googleapis.com\n<\/code><\/pre>\n\n\n\n<p><strong>Verify<\/strong><\/p>\n\n\n\n<pre><code class=\"language-bash\">gcloud services list --enabled --filter=\"name:aiplatform.googleapis.com OR name:bigquery.googleapis.com OR name:storage.googleapis.com\"\n<\/code><\/pre>\n\n\n\n<h3 class=\"wp-block-heading\">Step 2: Create a Cloud Storage bucket for staging<\/h3>\n\n\n\n<p><strong>Expected outcome<\/strong>: A bucket exists to store a small CSV file.<\/p>\n\n\n\n<p>Choose a globally unique bucket name:<\/p>\n\n\n\n<pre><code class=\"language-bash\">export BUCKET=\"YOUR_PROJECT_ID-vertex-datasets-lab\"\n<\/code><\/pre>\n\n\n\n<p>Create the bucket (regional to match your Vertex AI region where possible):<\/p>\n\n\n\n<pre><code class=\"language-bash\">gcloud storage buckets create gs:\/\/$BUCKET --location=$REGION\n<\/code><\/pre>\n\n\n\n<p><strong>Verify<\/strong><\/p>\n\n\n\n<pre><code class=\"language-bash\">gcloud storage buckets describe gs:\/\/$BUCKET\n<\/code><\/pre>\n\n\n\n<h3 class=\"wp-block-heading\">Step 3: Create a small CSV dataset locally and upload it<\/h3>\n\n\n\n<p><strong>Expected outcome<\/strong>: You have a CSV in Cloud Storage.<\/p>\n\n\n\n<p>Create a file named <code>customer_churn_sample.csv<\/code>:<\/p>\n\n\n\n<pre><code class=\"language-bash\">cat &gt; customer_churn_sample.csv &lt;&lt; 'EOF'\ncustomer_id,tenure_months,monthly_charges,has_internet,contract_type,churned\nC001,1,29.85,true,month-to-month,true\nC002,34,56.95,true,one-year,false\nC003,2,53.85,true,month-to-month,true\nC004,45,42.30,false,two-year,false\nC005,8,70.70,true,month-to-month,true\nC006,22,89.10,true,one-year,false\nC007,60,25.00,false,two-year,false\nC008,12,99.65,true,month-to-month,true\nEOF\n<\/code><\/pre>\n\n\n\n<p>Upload it:<\/p>\n\n\n\n<pre><code class=\"language-bash\">gcloud storage cp customer_churn_sample.csv gs:\/\/$BUCKET\/\n<\/code><\/pre>\n\n\n\n<p><strong>Verify<\/strong><\/p>\n\n\n\n<pre><code class=\"language-bash\">gcloud storage ls gs:\/\/$BUCKET\/\n<\/code><\/pre>\n\n\n\n<h3 class=\"wp-block-heading\">Step 4: Create a BigQuery dataset and load the CSV into a table<\/h3>\n\n\n\n<p><strong>Expected outcome<\/strong>: BigQuery dataset + table exists and contains rows.<\/p>\n\n\n\n<p>1) Create a BigQuery dataset (use <code>US<\/code> multi-region for simplicity if you picked a US Vertex AI region).<br\/>\nIf you are using an EU Vertex AI region, use <code>EU<\/code> instead.<\/p>\n\n\n\n<pre><code class=\"language-bash\">export BQ_LOCATION=US\nexport BQ_DATASET=vertex_datasets_lab\n\nbq --location=$BQ_LOCATION mk -d \\\n  --description \"Vertex AI Datasets lab dataset\" \\\n  $BQ_DATASET\n<\/code><\/pre>\n\n\n\n<p>2) Load the CSV from Cloud Storage into a table:<\/p>\n\n\n\n<pre><code class=\"language-bash\">bq --location=$BQ_LOCATION load \\\n  --source_format=CSV \\\n  --skip_leading_rows=1 \\\n  --autodetect \\\n  ${BQ_DATASET}.customer_churn_sample \\\n  gs:\/\/$BUCKET\/customer_churn_sample.csv\n<\/code><\/pre>\n\n\n\n<p>3) Query to confirm rows:<\/p>\n\n\n\n<pre><code class=\"language-bash\">bq --location=$BQ_LOCATION query --use_legacy_sql=false \\\n  \"SELECT contract_type, COUNT(*) AS n, SUM(CAST(churned AS INT64)) AS churned\n   FROM \\`${BQ_DATASET}.customer_churn_sample\\`\n   GROUP BY contract_type\n   ORDER BY n DESC;\"\n<\/code><\/pre>\n\n\n\n<p><strong>Notes on locations<\/strong>\n&#8211; BigQuery datasets are created in locations like <code>US<\/code>, <code>EU<\/code>, or a specific region.\n&#8211; Vertex AI datasets are created in a Vertex AI region (like <code>us-central1<\/code>).\n&#8211; Location compatibility can matter for some workflows. If you hit location-related errors later, align BigQuery dataset region with your Vertex AI region as closely as possible (or follow Google\u2019s recommended compatible location combinations in official docs).<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Step 5: Create a Vertex AI Datasets tabular dataset (Console)<\/h3>\n\n\n\n<p>Using the console avoids having to specify schema URIs and import schema URIs.<\/p>\n\n\n\n<p><strong>Expected outcome<\/strong>: A Vertex AI Dataset exists in your chosen region.<\/p>\n\n\n\n<p>1) Open the Vertex AI Datasets page:<br\/>\nhttps:\/\/console.cloud.google.com\/vertex-ai\/datasets<\/p>\n\n\n\n<p>2) Select the same project and confirm the region (top bar or dataset creation flow).<\/p>\n\n\n\n<p>3) Click <strong>Create dataset<\/strong>.<\/p>\n\n\n\n<p>4) Configure:\n&#8211; <strong>Dataset name<\/strong>: <code>customer_churn_tabular_lab<\/code>\n&#8211; <strong>Data type<\/strong>: <strong>Tabular<\/strong>\n&#8211; <strong>Select a data source<\/strong>: <strong>BigQuery<\/strong>\n&#8211; Choose the table:\n  &#8211; Dataset: <code>vertex_datasets_lab<\/code>\n  &#8211; Table: <code>customer_churn_sample<\/code><\/p>\n\n\n\n<p>5) Create\/import.<\/p>\n\n\n\n<p><strong>Verify in console<\/strong>\n&#8211; You should see the dataset appear in the datasets list.\n&#8211; Open it and confirm you see schema\/columns and the data source reference.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Step 6: Verify with gcloud CLI<\/h3>\n\n\n\n<p><strong>Expected outcome<\/strong>: You can list and describe the dataset resource.<\/p>\n\n\n\n<p>List datasets in the region:<\/p>\n\n\n\n<pre><code class=\"language-bash\">gcloud ai datasets list --region=$REGION\n<\/code><\/pre>\n\n\n\n<p>Describe the dataset (replace <code>DATASET_ID<\/code> with the ID from the list output):<\/p>\n\n\n\n<pre><code class=\"language-bash\">export DATASET_ID=\"PASTE_DATASET_ID_HERE\"\n\ngcloud ai datasets describe $DATASET_ID --region=$REGION\n<\/code><\/pre>\n\n\n\n<p>You should see fields like:\n&#8211; name (resource name)\n&#8211; displayName\n&#8211; createTime\n&#8211; metadataSchemaUri (internal schema reference)<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Validation<\/h3>\n\n\n\n<p>You have successfully completed the lab if:\n&#8211; BigQuery table <code>vertex_datasets_lab.customer_churn_sample<\/code> exists and returns rows.\n&#8211; Vertex AI dataset <code>customer_churn_tabular_lab<\/code> exists in the Vertex AI console.\n&#8211; <code>gcloud ai datasets list<\/code> shows your dataset.\n&#8211; <code>gcloud ai datasets describe<\/code> returns dataset details without permission errors.<\/p>\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<p>1) <strong>Permission denied creating dataset<\/strong>\n&#8211; Cause: Missing Vertex AI role.\n&#8211; Fix: Grant <code>roles\/aiplatform.user<\/code> (or admin) to your user\/service account.<\/p>\n\n\n\n<p>2) <strong>Permission denied reading BigQuery table<\/strong>\n&#8211; Cause: You can create the Vertex AI dataset but can\u2019t access the BigQuery table.\n&#8211; Fix: Grant <code>roles\/bigquery.dataViewer<\/code> on the dataset\/table.<\/p>\n\n\n\n<p>3) <strong>Location mismatch errors<\/strong>\n&#8211; Cause: BigQuery dataset in <code>EU<\/code>, Vertex AI region in US (or vice versa), or incompatible combination.\n&#8211; Fix: Recreate the BigQuery dataset in a compatible location, or choose a Vertex AI region aligned with your data.<\/p>\n\n\n\n<p>4) <strong>API not enabled<\/strong>\n&#8211; Cause: <code>aiplatform.googleapis.com<\/code> not enabled.\n&#8211; Fix: Enable it with <code>gcloud services enable aiplatform.googleapis.com<\/code>.<\/p>\n\n\n\n<p>5) <strong><code>gcloud ai datasets<\/code> command not found<\/strong>\n&#8211; Cause: Old Cloud SDK components.\n&#8211; Fix: Update Cloud SDK:\n  <code>bash\n  gcloud components update<\/code><\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Cleanup<\/h3>\n\n\n\n<p>To avoid ongoing costs, delete created resources.<\/p>\n\n\n\n<p>1) Delete the Vertex AI dataset:<\/p>\n\n\n\n<pre><code class=\"language-bash\">gcloud ai datasets delete $DATASET_ID --region=$REGION --quiet\n<\/code><\/pre>\n\n\n\n<p>2) Delete BigQuery dataset (deletes tables inside):<\/p>\n\n\n\n<pre><code class=\"language-bash\">bq --location=$BQ_LOCATION rm -r -f $BQ_DATASET\n<\/code><\/pre>\n\n\n\n<p>3) Delete the Cloud Storage bucket (must be empty first):<\/p>\n\n\n\n<pre><code class=\"language-bash\">gcloud storage rm -r gs:\/\/$BUCKET\n<\/code><\/pre>\n\n\n\n<p>4) Optional: remove local file:<\/p>\n\n\n\n<pre><code class=\"language-bash\">rm -f customer_churn_sample.csv\n<\/code><\/pre>\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>Align locations<\/strong>: Keep Vertex AI dataset region aligned with BigQuery dataset location and Cloud Storage bucket location to reduce latency and avoid cross-region constraints.<\/li>\n<li><strong>Separate raw vs curated<\/strong>: Store raw data in a raw zone, curate a stable training dataset, and reference the curated dataset from Vertex AI Datasets.<\/li>\n<li><strong>Design for reproducibility<\/strong>:<\/li>\n<li>Use immutable BigQuery tables (or snapshots) for training inputs.<\/li>\n<li>Use partitioned tables and explicit partitions when appropriate.<\/li>\n<li>Use naming conventions like <code>features_vYYYYMMDD<\/code> or <code>features_v3<\/code>.<\/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 least privilege:<\/li>\n<li>dataset viewers should not automatically be bucket admins<\/li>\n<li>separate \u201cdataset metadata admin\u201d from \u201cdata plane access\u201d where possible<\/li>\n<li>Prefer group-based access (Google Groups \/ Cloud Identity).<\/li>\n<li>Use service accounts for automation (CI\/CD) with narrow roles.<\/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>Avoid duplicating large datasets for experiments; use subsets or views carefully.<\/li>\n<li>For BigQuery:<\/li>\n<li>Minimize repeated full scans (use partitioning and clustering).<\/li>\n<li>Consider materialized views for recurring features if it reduces processing.<\/li>\n<li>For Cloud Storage:<\/li>\n<li>Set lifecycle rules for intermediate artifacts.<\/li>\n<li>Choose storage class based on access patterns.<\/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 data close to compute (region alignment).<\/li>\n<li>Avoid cross-region reads during training.<\/li>\n<li>For tabular sources, optimize BigQuery table layout (partitioning\/clustering) when query-based prep is used.<\/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>Treat dataset creation\/import as code where possible (SDK\/CLI).<\/li>\n<li>Use CI validation steps:<\/li>\n<li>check table schema compatibility<\/li>\n<li>check row counts and null rates<\/li>\n<li>confirm IAM access<\/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 labels on dataset resources for:<\/li>\n<li><code>env=dev|prod<\/code><\/li>\n<li><code>owner=team-x<\/code><\/li>\n<li><code>cost-center=...<\/code><\/li>\n<li>Monitor:<\/li>\n<li>failed import\/labeling jobs<\/li>\n<li>permission-related errors in logs<\/li>\n<li>Document dataset contracts:<\/li>\n<li>schema expectations<\/li>\n<li>label definitions<\/li>\n<li>update cadence<\/li>\n<li>known caveats<\/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>Naming pattern example:<\/li>\n<li><code>domain_modality_purpose_env<\/code><br\/>\n  e.g., <code>support_text_intent_prod<\/code><\/li>\n<li>Tag underlying BigQuery tables and GCS buckets with consistent labels.<\/li>\n<li>For sensitive data, formalize:<\/li>\n<li>retention policy<\/li>\n<li>access approval workflow<\/li>\n<li>de-identification controls<\/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<ul class=\"wp-block-list\">\n<li>Vertex AI Datasets access is controlled by <strong>IAM<\/strong> on Vertex AI resources.<\/li>\n<li>Underlying data access is controlled separately:<\/li>\n<li>BigQuery IAM for datasets\/tables<\/li>\n<li>Cloud Storage IAM for buckets\/objects<\/li>\n<\/ul>\n\n\n\n<p><strong>Secure design principle<\/strong>: grant access to the dataset resource only to users who also have the appropriate access to the data source\u2014and vice versa.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Encryption<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Google Cloud encrypts data at rest and in transit by default across managed services.<\/li>\n<li>If you require customer-managed encryption keys (CMEK), verify:<\/li>\n<li>whether CMEK applies to Vertex AI dataset metadata and\/or to related jobs,<\/li>\n<li>and how it applies to your BigQuery tables and Cloud Storage buckets.<br\/>\n  CMEK support varies by product and region\u2014<strong>verify in official docs<\/strong>.<\/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>Access is via Google APIs; secure it with:<\/li>\n<li>IAM<\/li>\n<li>organization policy constraints<\/li>\n<li>VPC Service Controls (common for sensitive ML environments)<\/li>\n<li>If running from GCE\/GKE without external IPs, use <strong>Private Google Access<\/strong> to reach Google APIs.<\/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>Don\u2019t embed credentials in notebooks\/scripts.<\/li>\n<li>Use:<\/li>\n<li>Workload Identity (GKE) or service accounts (GCE\/Cloud Run)<\/li>\n<li>Secret Manager for API keys\/secrets (when 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>Enable and retain <strong>Cloud Audit Logs<\/strong> according to your compliance needs.<\/li>\n<li>Ensure dataset create\/import\/delete actions are logged and reviewable.<\/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 Vertex AI region and data locations that match regulatory requirements.<\/li>\n<li>PII\/PHI: apply de-identification, DLP scanning, and strict IAM on underlying data stores.<\/li>\n<li>Vendor labeling: if you use external labelers, ensure contractual and technical controls.<\/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>Giving <code>roles\/storage.admin<\/code> broadly just to \u201cfix access.\u201d<\/li>\n<li>Putting sensitive training data in public buckets or overly permissive IAM.<\/li>\n<li>Mixing dev\/prod data in the same bucket without clear separation and controls.<\/li>\n<li>Not aligning VPC Service Controls perimeters across Vertex AI, BigQuery, and Storage.<\/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 dev\/test\/prod.<\/li>\n<li>Apply org policies (e.g., restrict external IPs, restrict service account key creation).<\/li>\n<li>Use VPC Service Controls for sensitive environments.<\/li>\n<li>Use structured approvals for dataset promotion to production.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">13. Limitations and Gotchas<\/h2>\n\n\n\n<blockquote>\n<p>Always validate current limits and supported formats in official docs. Limits and capabilities evolve.<\/p>\n<\/blockquote>\n\n\n\n<p>Common limitations\/gotchas include:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Region and location constraints<\/strong><\/li>\n<li>Vertex AI dataset resources are regional.<\/li>\n<li>BigQuery and Cloud Storage sources have locations; mismatches can cause issues.<\/li>\n<li><strong>Dataset is not a data warehouse<\/strong><\/li>\n<li>Vertex AI Datasets is not meant to replace BigQuery or a data lake.<\/li>\n<li><strong>Not a full governance\/catalog solution<\/strong><\/li>\n<li>Use Dataplex\/Data Catalog for broader governance and discovery.<\/li>\n<li><strong>Underlying access still required<\/strong><\/li>\n<li>Having permission to a dataset resource doesn\u2019t automatically grant permission to the BigQuery table or GCS objects.<\/li>\n<li><strong>Quota constraints<\/strong><\/li>\n<li>API rate limits and resource quotas can affect automation at scale. Check quotas.<\/li>\n<li><strong>Import format requirements<\/strong><\/li>\n<li>Image\/text\/video dataset imports often require specific manifest\/CSV formats depending on the task. Verify the current required formats.<\/li>\n<li><strong>Pricing surprises<\/strong><\/li>\n<li>Labeling and training can become the dominant cost quickly.<\/li>\n<li>BigQuery repeated scans during feature creation can be expensive.<\/li>\n<li><strong>Migration challenges<\/strong><\/li>\n<li>If you migrate from another MLOps platform, you may need to re-map dataset identifiers and re-import metadata.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">14. Comparison with Alternatives<\/h2>\n\n\n\n<p>Vertex AI Datasets is part of the Vertex AI ecosystem; alternatives depend on whether you need ML dataset metadata management, labeling integration, or general data governance.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Comparison table<\/h3>\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 Datasets (Google Cloud)<\/strong><\/td>\n<td>Teams standardizing ML workflows on Vertex AI<\/td>\n<td>Native integration with Vertex AI training\/AutoML and labeling; regional resource control; IAM integration<\/td>\n<td>Not a full data governance tool; relies on underlying stores; modality-specific import formats<\/td>\n<td>You use Vertex AI for training\/MLOps and want a dataset registry tied to ML workflows<\/td>\n<\/tr>\n<tr>\n<td><strong>BigQuery (tables\/views) + conventions<\/strong><\/td>\n<td>Tabular-only ML with strong SQL governance<\/td>\n<td>Great analytics, governance controls, performance, lineage tooling<\/td>\n<td>No ML-native dataset object for multi-modality; labeling not native<\/td>\n<td>Your ML is tabular and you already manage \u201ctraining tables\u201d well in BigQuery<\/td>\n<\/tr>\n<tr>\n<td><strong>Cloud Storage + folder conventions<\/strong><\/td>\n<td>File-based datasets and simple pipelines<\/td>\n<td>Simple, cheap, flexible<\/td>\n<td>Easy to lose track of versions\/labels; governance is manual<\/td>\n<td>Small teams or early-stage projects, or as the underlying storage layer<\/td>\n<\/tr>\n<tr>\n<td><strong>Dataplex \/ Data Catalog (Google Cloud)<\/strong><\/td>\n<td>Enterprise governance and discovery<\/td>\n<td>Governance, cataloging, policies, lineage (for supported sources)<\/td>\n<td>Not a replacement for ML dataset objects and labeling workflows<\/td>\n<td>You need enterprise-wide governance plus ML workflows\u2014use alongside Vertex AI Datasets<\/td>\n<\/tr>\n<tr>\n<td><strong>Vertex AI Feature Store<\/strong> (if used)<\/td>\n<td>Serving\/monitoring ML features<\/td>\n<td>Feature reuse and online\/offline serving patterns<\/td>\n<td>Not a general dataset registry; different scope<\/td>\n<td>You need feature management for training\/serving consistency (complementary, not a substitute)<\/td>\n<\/tr>\n<tr>\n<td><strong>AWS SageMaker (Data Wrangler \/ Ground Truth \/ Feature Store)<\/strong><\/td>\n<td>AWS-native ML platform<\/td>\n<td>Tight AWS integration and labeling (Ground Truth)<\/td>\n<td>Different cloud ecosystem; migration overhead<\/td>\n<td>Your stack is on AWS and you want native dataset\/labeling tooling there<\/td>\n<\/tr>\n<tr>\n<td><strong>Azure Machine Learning Data assets<\/strong><\/td>\n<td>Azure-native ML platform<\/td>\n<td>Data asset registry integrated with AML<\/td>\n<td>Different ecosystem; migration overhead<\/td>\n<td>Your stack is on Azure ML<\/td>\n<\/tr>\n<tr>\n<td><strong>DVC \/ lakeFS (self-managed)<\/strong><\/td>\n<td>Git-like dataset versioning and branching<\/td>\n<td>Strong dataset versioning semantics; toolchain flexibility<\/td>\n<td>Operational overhead; integration work<\/td>\n<td>You need advanced dataset versioning and are willing to run\/operate tooling<\/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 customer-risk modeling<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Problem<\/strong>: A bank trains multiple risk models with strict audit requirements. Data lives in BigQuery with tight controls. Teams need consistent dataset references and repeatable retraining.<\/li>\n<li><strong>Proposed architecture<\/strong><\/li>\n<li>BigQuery hosts curated feature tables (partitioned by snapshot date).<\/li>\n<li>Vertex AI Datasets registers a tabular dataset per model family, referencing the curated table or snapshot tables.<\/li>\n<li>Vertex AI Pipelines orchestrates monthly snapshot creation \u2192 dataset update\/import \u2192 training \u2192 evaluation \u2192 registry.<\/li>\n<li>IAM groups enforce who can view datasets and who can access underlying BigQuery tables.<\/li>\n<li>Cloud Audit Logs retained to support audits.<\/li>\n<li><strong>Why Vertex AI Datasets was chosen<\/strong><\/li>\n<li>Provides a consistent, Vertex-AI-native dataset object for pipelines and training.<\/li>\n<li>Simplifies reproducibility and reduces \u201cwrong input table\u201d errors.<\/li>\n<li><strong>Expected outcomes<\/strong><\/li>\n<li>More repeatable retraining.<\/li>\n<li>Cleaner audit story (dataset IDs + table snapshot references).<\/li>\n<li>Faster onboarding for new ML engineers.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Startup\/small-team example: ecommerce image categorization<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Problem<\/strong>: A startup needs to classify product images into categories. Images are in Cloud Storage; labels are evolving.<\/li>\n<li><strong>Proposed architecture<\/strong><\/li>\n<li>Cloud Storage bucket holds product images.<\/li>\n<li>Vertex AI Datasets stores an image dataset with label metadata.<\/li>\n<li>Vertex AI Data Labeling (optional) used in small batches to improve labels.<\/li>\n<li>AutoML training triggered when label quality reaches threshold.<\/li>\n<li><strong>Why Vertex AI Datasets was chosen<\/strong><\/li>\n<li>Minimal operational overhead compared to building a custom dataset registry.<\/li>\n<li>Tight path from dataset \u2192 labeling \u2192 training.<\/li>\n<li><strong>Expected outcomes<\/strong><\/li>\n<li>Faster iteration on label taxonomy.<\/li>\n<li>Repeatable training input.<\/li>\n<li>Reduced manual data management.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">16. FAQ<\/h2>\n\n\n\n<p>1) <strong>Is Vertex AI Datasets the same as a BigQuery dataset?<\/strong><br\/>\nNo. A BigQuery dataset is a container for BigQuery tables. Vertex AI Datasets is an ML dataset resource in Vertex AI that references data in BigQuery and\/or Cloud Storage (depending on type) and stores ML-specific metadata.<\/p>\n\n\n\n<p>2) <strong>Does Vertex AI Datasets copy my data into Vertex AI?<\/strong><br\/>\nUsually, it stores metadata and references to underlying data (GCS URIs or BigQuery tables). Exact behavior can vary by dataset type and workflow\u2014verify in official docs for your modality and import method.<\/p>\n\n\n\n<p>3) <strong>Is Vertex AI Datasets required to train models on Vertex AI?<\/strong><br\/>\nNot always. Many custom training workflows can read directly from GCS\/BigQuery. Vertex AI Datasets is most helpful for standardized workflows, reuse, and labeling\/AutoML integration.<\/p>\n\n\n\n<p>4) <strong>What dataset types are supported (tabular\/image\/text\/video)?<\/strong><br\/>\nVertex AI commonly supports tabular, image, text, and video datasets, but exact supported tasks, formats, and regions can change. Verify in: https:\/\/cloud.google.com\/vertex-ai\/docs\/datasets\/introduction<\/p>\n\n\n\n<p>5) <strong>Are Vertex AI datasets global or regional?<\/strong><br\/>\nThey are <strong>regional<\/strong> resources in a specified Vertex AI location.<\/p>\n\n\n\n<p>6) <strong>Can I move a dataset to another region?<\/strong><br\/>\nTypically you recreate the dataset in the target region and re-import from the source data. Verify whether any migration tooling exists for your dataset type.<\/p>\n\n\n\n<p>7) <strong>How do permissions work?<\/strong><br\/>\nYou need IAM permissions for:\n&#8211; the Vertex AI dataset resource (Vertex AI roles),\n&#8211; and the underlying data (BigQuery roles and\/or Cloud Storage roles).<\/p>\n\n\n\n<p>8) <strong>Can multiple projects share the same Vertex AI dataset?<\/strong><br\/>\nVertex AI datasets are project-scoped. Cross-project sharing is usually done by sharing the underlying data (BQ\/GCS) and recreating dataset resources in each project, or by centralizing ML in one project. Design depends on org policies.<\/p>\n\n\n\n<p>9) <strong>How do I version datasets?<\/strong><br\/>\nVertex AI Datasets is primarily a dataset resource\/metadata layer. For versioning, teams often use:\n&#8211; BigQuery snapshot tables or partitioned snapshots,\n&#8211; GCS object versioning and manifests,\n&#8211; and MLOps metadata in pipelines.<br\/>\nVerify if any native dataset version features exist for your dataset type in current docs.<\/p>\n\n\n\n<p>10) <strong>What\u2019s the difference between Vertex AI Datasets and Vertex AI Feature Store?<\/strong><br\/>\nDatasets manage training\/evaluation data assets; Feature Store (where used) focuses on feature reuse and online\/offline feature serving patterns. They solve different problems and are often complementary.<\/p>\n\n\n\n<p>11) <strong>Can I use VPC Service Controls with Vertex AI Datasets?<\/strong><br\/>\nMany enterprises use VPC SC with Vertex AI, BigQuery, and Cloud Storage. Verify the latest supported configurations in official VPC SC docs and Vertex AI docs.<\/p>\n\n\n\n<p>12) <strong>What\u2019s the cheapest way to try Vertex AI Datasets?<\/strong><br\/>\nCreate a small tabular dataset referencing a small BigQuery table and avoid training\/labeling jobs until you\u2019re ready.<\/p>\n\n\n\n<p>13) <strong>Does using Vertex AI Datasets improve model accuracy?<\/strong><br\/>\nNot directly. It improves manageability, consistency, and operational reliability, which can indirectly improve outcomes by reducing data mistakes and supporting better iteration.<\/p>\n\n\n\n<p>14) <strong>How do I automate dataset creation?<\/strong><br\/>\nUse the Vertex AI API, <code>gcloud ai datasets<\/code> commands, or the Vertex AI Python SDK. Validate quotas and IAM.<\/p>\n\n\n\n<p>15) <strong>What should I monitor in production?<\/strong><br\/>\nMonitor:\n&#8211; import\/labeling job failures,\n&#8211; permission errors,\n&#8211; underlying data pipeline health (BigQuery jobs, Dataflow pipelines),\n&#8211; cost anomalies (BigQuery scans, labeling spend, training runs).<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">17. Top Online Resources to Learn Vertex AI Datasets<\/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 Datasets introduction \u2014 https:\/\/cloud.google.com\/vertex-ai\/docs\/datasets\/introduction<\/td>\n<td>Canonical overview of dataset concepts, types, and workflows<\/td>\n<\/tr>\n<tr>\n<td>Official documentation<\/td>\n<td>Vertex AI Access control (IAM) \u2014 https:\/\/cloud.google.com\/vertex-ai\/docs\/general\/access-control<\/td>\n<td>Role guidance and permission model for Vertex AI resources<\/td>\n<\/tr>\n<tr>\n<td>Official CLI reference<\/td>\n<td><code>gcloud ai datasets<\/code> reference \u2014 https:\/\/cloud.google.com\/sdk\/gcloud\/reference\/ai\/datasets<\/td>\n<td>Command syntax for listing\/creating\/describing\/deleting datasets<\/td>\n<\/tr>\n<tr>\n<td>Official pricing<\/td>\n<td>Vertex AI pricing \u2014 https:\/\/cloud.google.com\/vertex-ai\/pricing<\/td>\n<td>Current pricing model for Vertex AI services<\/td>\n<\/tr>\n<tr>\n<td>Official pricing tool<\/td>\n<td>Google Cloud Pricing Calculator \u2014 https:\/\/cloud.google.com\/products\/calculator<\/td>\n<td>Region-specific estimates without guessing<\/td>\n<\/tr>\n<tr>\n<td>Official docs<\/td>\n<td>Vertex AI Quotas \u2014 https:\/\/cloud.google.com\/vertex-ai\/quotas<\/td>\n<td>Quota limits and how to request increases<\/td>\n<\/tr>\n<tr>\n<td>Official docs<\/td>\n<td>Vertex AI Data Labeling overview \u2014 https:\/\/cloud.google.com\/vertex-ai\/docs\/data-labeling\/overview<\/td>\n<td>How labeling integrates with datasets and what to expect operationally<\/td>\n<\/tr>\n<tr>\n<td>Official BigQuery pricing<\/td>\n<td>BigQuery pricing \u2014 https:\/\/cloud.google.com\/bigquery\/pricing<\/td>\n<td>Key cost drivers if you use BigQuery as a dataset source<\/td>\n<\/tr>\n<tr>\n<td>Official Cloud Storage pricing<\/td>\n<td>Cloud Storage pricing \u2014 https:\/\/cloud.google.com\/storage\/pricing<\/td>\n<td>Key cost drivers for file-based datasets<\/td>\n<\/tr>\n<tr>\n<td>Official architecture guidance<\/td>\n<td>MLOps on Google Cloud (Architecture Center) \u2014 https:\/\/cloud.google.com\/architecture\/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning<\/td>\n<td>Reference architecture for dataset\u2192pipeline\u2192training operationalization<\/td>\n<\/tr>\n<tr>\n<td>Official SDK docs<\/td>\n<td>Vertex AI Python SDK reference \u2014 https:\/\/cloud.google.com\/python\/docs\/reference\/aiplatform\/latest<\/td>\n<td>Programmatic dataset operations and end-to-end ML automation<\/td>\n<\/tr>\n<tr>\n<td>Official samples (GitHub)<\/td>\n<td>GoogleCloudPlatform vertex-ai samples \u2014 https:\/\/github.com\/GoogleCloudPlatform\/vertex-ai-samples<\/td>\n<td>Practical notebooks and code patterns (verify dataset examples relevant to your modality)<\/td>\n<\/tr>\n<tr>\n<td>Official videos<\/td>\n<td>Google Cloud Tech (YouTube) \u2014 https:\/\/www.youtube.com\/@googlecloudtech<\/td>\n<td>Product walkthroughs; search within channel for Vertex AI datasets\/labeling<\/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<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\/Platform engineers, cloud engineers, SREs<\/td>\n<td>MLOps\/DevOps practices, automation, Google Cloud operations basics<\/td>\n<td>check website<\/td>\n<td>https:\/\/www.devopsschool.com\/<\/td>\n<\/tr>\n<tr>\n<td>ScmGalaxy.com<\/td>\n<td>Developers, build\/release engineers, platform teams<\/td>\n<td>SCM\/CI\/CD concepts, automation practices that support 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 teams, sysadmins<\/td>\n<td>Cloud operations fundamentals, operational readiness<\/td>\n<td>check website<\/td>\n<td>https:\/\/www.cloudopsnow.in\/<\/td>\n<\/tr>\n<tr>\n<td>SreSchool.com<\/td>\n<td>SREs, reliability engineers, platform teams<\/td>\n<td>Reliability engineering practices applicable to ML platforms<\/td>\n<td>check website<\/td>\n<td>https:\/\/www.sreschool.com\/<\/td>\n<\/tr>\n<tr>\n<td>AiOpsSchool.com<\/td>\n<td>Ops teams adopting AIOps, ML ops<\/td>\n<td>Monitoring\/automation practices; AIOps concepts that can complement ML operations<\/td>\n<td>check website<\/td>\n<td>https:\/\/www.aiopsschool.com\/<\/td>\n<\/tr>\n<\/tbody>\n<\/table><\/figure>\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>Cloud\/DevOps training content (verify specific Vertex AI coverage)<\/td>\n<td>Beginners to intermediate engineers<\/td>\n<td>https:\/\/rajeshkumar.xyz\/<\/td>\n<\/tr>\n<tr>\n<td>devopstrainer.in<\/td>\n<td>DevOps training and workshops<\/td>\n<td>DevOps engineers, platform teams<\/td>\n<td>https:\/\/www.devopstrainer.in\/<\/td>\n<\/tr>\n<tr>\n<td>devopsfreelancer.com<\/td>\n<td>Freelance DevOps\/automation help (as a platform)<\/td>\n<td>Teams needing short-term expertise<\/td>\n<td>https:\/\/www.devopsfreelancer.com\/<\/td>\n<\/tr>\n<tr>\n<td>devopssupport.in<\/td>\n<td>DevOps support and training resources<\/td>\n<td>Ops teams and engineers<\/td>\n<td>https:\/\/www.devopssupport.in\/<\/td>\n<\/tr>\n<\/tbody>\n<\/table><\/figure>\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 Name<\/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 current offerings)<\/td>\n<td>Cloud adoption, automation, platform engineering<\/td>\n<td>Designing CI\/CD for ML pipelines, IAM hardening, cost reviews<\/td>\n<td>https:\/\/cotocus.com\/<\/td>\n<\/tr>\n<tr>\n<td>DevOpsSchool.com<\/td>\n<td>DevOps and cloud consulting\/training<\/td>\n<td>Team enablement, DevOps transformation<\/td>\n<td>Building operational runbooks, setting up observability, improving deployment practices<\/td>\n<td>https:\/\/www.devopsschool.com\/<\/td>\n<\/tr>\n<tr>\n<td>DEVOPSCONSULTING.IN<\/td>\n<td>DevOps consulting (verify current offerings)<\/td>\n<td>CI\/CD, infrastructure automation, reliability practices<\/td>\n<td>Automation pipelines, infrastructure-as-code standardization, production readiness reviews<\/td>\n<td>https:\/\/www.devopsconsulting.in\/<\/td>\n<\/tr>\n<\/tbody>\n<\/table><\/figure>\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 Datasets<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Google Cloud fundamentals:<\/li>\n<li>projects, IAM, service accounts, billing<\/li>\n<li>Cloud Storage basics (buckets, IAM, lifecycle)<\/li>\n<li>BigQuery basics (datasets, tables, locations, pricing)<\/li>\n<li>ML fundamentals:<\/li>\n<li>supervised learning concepts<\/li>\n<li>train\/validation\/test splits<\/li>\n<li>feature engineering basics<\/li>\n<li>Basic MLOps concepts:<\/li>\n<li>reproducibility<\/li>\n<li>data lineage<\/li>\n<li>automation and CI\/CD<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">What to learn after Vertex AI Datasets<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Vertex AI training options:<\/li>\n<li>AutoML (where applicable)<\/li>\n<li>Custom training jobs<\/li>\n<li>Vertex AI Pipelines for orchestration<\/li>\n<li>Model Registry and model deployment patterns<\/li>\n<li>Monitoring and drift detection patterns (Vertex AI Model Monitoring where applicable)<\/li>\n<li>Data governance on Google Cloud (Dataplex, IAM Conditions, DLP)<\/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 \/ Senior ML Engineer<\/li>\n<li>Cloud Engineer supporting AI platforms<\/li>\n<li>Data Engineer collaborating with ML teams<\/li>\n<li>Platform Engineer \/ MLOps Engineer<\/li>\n<li>SRE supporting ML systems<\/li>\n<li>Security Engineer reviewing AI\/ML data access patterns<\/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. Commonly relevant tracks include:\n&#8211; Professional Machine Learning Engineer\n&#8211; Professional Cloud Architect\n&#8211; Associate Cloud Engineer<\/p>\n\n\n\n<p>Verify current certification names and requirements here:<br\/>\nhttps:\/\/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>Create a \u201cgolden dataset\u201d pattern:<\/li>\n<li>raw \u2192 curated BigQuery table \u2192 Vertex AI dataset \u2192 pipeline training<\/li>\n<li>Build a dataset importer script:<\/li>\n<li>validates schema and row counts<\/li>\n<li>creates\/updates dataset resources<\/li>\n<li>Implement least-privilege IAM:<\/li>\n<li>separate dataset viewers from data viewers<\/li>\n<li>audit with Cloud Logging queries<\/li>\n<li>Cost governance exercise:<\/li>\n<li>estimate BigQuery scan cost for feature creation<\/li>\n<li>optimize table partitioning and pipeline schedules<\/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>Vertex AI Datasets<\/strong>: Vertex AI service capability to create\/manage dataset resources used for ML workflows.<\/li>\n<li><strong>Dataset resource<\/strong>: A regional Vertex AI object that stores metadata and references to underlying data.<\/li>\n<li><strong>BigQuery dataset (BQ dataset)<\/strong>: A container of BigQuery tables (not the same as Vertex AI dataset).<\/li>\n<li><strong>Cloud Storage bucket<\/strong>: Storage container for objects (files) used by ML workflows.<\/li>\n<li><strong>Data item<\/strong>: An individual unit in a dataset (row\/file\/document\/clip) represented in dataset metadata.<\/li>\n<li><strong>Annotation\/label<\/strong>: Supervised learning metadata attached to data items (class label, bounding box, etc.).<\/li>\n<li><strong>IAM (Identity and Access Management)<\/strong>: Google Cloud access control system based on roles and permissions.<\/li>\n<li><strong>Service account<\/strong>: Non-human identity used by applications\/automation to call Google APIs.<\/li>\n<li><strong>Region\/location<\/strong>: Geographic placement for resources; Vertex AI datasets are regional.<\/li>\n<li><strong>VPC Service Controls<\/strong>: A Google Cloud security feature to reduce data exfiltration risk by defining service perimeters.<\/li>\n<li><strong>MLOps<\/strong>: Operational practices for deploying and maintaining ML systems (automation, monitoring, governance).<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">23. Summary<\/h2>\n\n\n\n<p>Vertex AI Datasets in <strong>Google Cloud<\/strong> (AI and ML category) is a managed way to create <strong>regional dataset resources<\/strong> that reference your ML data in <strong>BigQuery<\/strong> and <strong>Cloud Storage<\/strong>, and optionally store labeling\/annotation metadata. It matters because it standardizes dataset handling across teams, improves reproducibility, and integrates cleanly with Vertex AI training and MLOps workflows.<\/p>\n\n\n\n<p>From a cost perspective, dataset metadata is usually not the main driver; the real costs typically come from <strong>storage (BQ\/GCS)<\/strong>, <strong>labeling<\/strong>, and <strong>training<\/strong>, plus any data processing and cross-region transfer. From a security perspective, success depends on designing <strong>IAM for both the dataset resource and the underlying data<\/strong>, aligning regions\/locations, and enabling auditability.<\/p>\n\n\n\n<p>Use Vertex AI Datasets when you want a consistent dataset registry tightly integrated with Vertex AI workflows. Next step: connect your dataset to a controlled training workflow (Vertex AI training and\/or Vertex AI Pipelines) and apply production IAM, logging, and cost controls.<\/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-565","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\/565","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=565"}],"version-history":[{"count":0,"href":"https:\/\/www.devopsschool.com\/tutorials\/wp-json\/wp\/v2\/posts\/565\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.devopsschool.com\/tutorials\/wp-json\/wp\/v2\/media?parent=565"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.devopsschool.com\/tutorials\/wp-json\/wp\/v2\/categories?post=565"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.devopsschool.com\/tutorials\/wp-json\/wp\/v2\/tags?post=565"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}