Google Cloud Professional Machine Learning Engineer Walkthrough 11 APIs Architecture, Essentials, an

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Google Cloud Professional Machine Learning Engineer Walkthrough 11 APIs (Architecture, Essentials, and Quotas) - November 21 VIEW RECORDING: https://fathom.video/share/HEx_spkghSp33DMPBbjNZQY4xb2nL9YT Meeting Purpose Reviewing GCP APIs, service enablement, and core ML project setup. Key Takeaways - APIs are the foundation of GCP: All services are accessed via REST APIs, which must be explicitly enabled per project to control costs and reduce the security attack surface. - Critical ML APIs span several categories: Core ML (Vertex AI), Data (BigQuery), Infrastructure (Compute Engine), and Monitoring (Cloud Logging) are essential for building and operating ML systems. - Quotas prevent abuse and ensure fair resource distribution: Understand default limits (e.g., concurrent jobs, requests/min) and request increases for high-traffic enterprise workloads to avoid unexpected failures. - A systematic project setup is non-negotiable: A well-organized project with proper IAM, cost controls, and regional planning provides a secure, scalable, and cost-effective foundation. Topics API-Driven Architecture - GCP services (AI, BigQuery, Cloud Storage) are exposed via REST APIs, which are invoked by the console, CLI, and client libraries. - APIs are disabled by default to prevent accidental resource creation and reduce the security attack surface. - Enabling APIs: - Console: Navigation Menu → APIs & Services → Library → Search API → Click Enable (takes 1–2 mins). - CLI: Use gcloud services enable with the service name (e.g., aiplatform.googleapis.com). - Rationale: CLI automation ensures consistent configurations across environments (dev, staging, prod). Essential APIs for ML Workloads - Core ML: - Vertex AI API: Unified platform for training, prediction, pipelines, datasets, and model registry. - AI Platform API: Legacy service for organizations not yet on Vertex AI. - AutoML API: For AutoML services (image, text, tabular classification). - Data: - BigQuery API: Read training data, write prediction results. - Cloud Storage API: Store datasets, model artifacts, training outputs. - Cloud SQL Admin API: Manage Cloud SQL databases for feature stores or application data. - Infrastructure: - Compute Engine API: Create VMs for custom training infrastructure. - Container Registry API: Store Docker images for custom training containers. - Artifact Registry API: Modern, versatile registry for all artifact types. - Cloud Build API: Build and deploy resources in CI/CD pipelines. - Monitoring & Operations: - Cloud Logging API: Collect and analyze logs from training jobs and prediction endpoints. - Cloud Monitoring API: Create dashboards and alerts for ML system health. - Cloud Trace API: Debug latency issues in ML pipelines. API Quotas - Quotas are limits on request rates and resource usage to prevent abuse and ensure fair resource distribution. - Example: Vertex AI might limit a project to 50 concurrent training jobs or 1,000 prediction requests/minute per endpoint. - Default quotas are usually sufficient for development and small-scale production. - Requesting Increases: For enterprise deployments, request increases via the console (IAM & Admin → Quotas) with a business justification. - Why it matters: Hitting a quota limit can cause unexpected failures (e.g., a batch prediction job failing mid-run). Foundational ML Project Setup - A systematic initial setup is critical for a secure, organized, and cost-controlled foundation. - Key Setup Steps: 1. Project Creation: The fundamental organizing unit. 2. API Enablement: Enable all necessary services. 3. Service Accounts: Create for RBAC/service-based access, applying the principle of least privilege. 4. Storage Configuration: Set up Cloud Storage buckets. 5. Billing Configuration: Link a billing account and set up budgets. 6. Labeling: Apply labels for cost attribution and organization. 7. Regional Deployment Plan: Select regions based on compliance, performance, and user proximity. - Rationale: This structure is inherited by all subsequent resources (datasets, jobs, models), ensuring consistent governance and permissions. Next Steps - Jules: - Continue creating GCP ML Associate exam prep content. - Viewers: - Like, subscribe, and share the video. - Master GCP fundamentals (project organization, IAM, cost management, regional planning, API enablement) before tackling advanced ML topics.