Detailed Course Syllabus
Module 1: Machine Learning Pipelines with Vertex AI Pipelines
Phase 1- ✓Introduction to Kubeflow Pipelines (KFP) and automated workflow orchestration
- ✓Defining pipeline components for data ingestion, preprocessing, and model training
- ✓Vertex AI Pipeline execution, lineage tracking, and metadata management
- ✓Scheduling automated model re-training jobs on Google Cloud
Module 2: BigData Engineering for AI on Google Cloud
Phase 2- ✓Designing scalable data warehouses with Google BigQuery and BigQuery ML
- ✓Real-time event streaming and data integration using Pub/Sub and Apache Beam on Dataflow
- ✓Feature engineering and storing vectors in Vertex AI Feature Store
- ✓Database optimization for RAG pipelines with Cloud SQL (pgvector) and AlloyDB
Module 3: Continuous Integration & Deployment for AI (CI/CD)
Phase 3- ✓Automating build pipelines for ML and LLM code using Google Cloud Build
- ✓Deploying scalable, containerized model endpoints using GKE (Google Kubernetes Engine)
- ✓Implementing blue-green and canary model deployments for high-availability production
- ✓Securing machine learning APIs with Google Cloud Endpoints and API Gateway
Module 4: Model Monitoring, Safety & Compliance
Phase 4- ✓Tracking feature drift and prediction skew with Vertex AI Model Monitoring
- ✓Implementing safety, content moderation, and toxicity filters on Generative AI outputs
- ✓Logging and auditing system access and AI request parameters with Cloud Audit Logs
- ✓Final Capstone Project: Automated CI/CD pipeline that builds, tests, and deploys a fine-tuned model
Download Official Syllabus
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Download CurriculumCourse Key Highlights
- Automate Pipelines with Vertex AI
- BigQuery ML & Dataflow Pipelines
- Continuous AI Integration (CI/CD)
- Vertex AI Model Monitoring
- 100% Practical Hands-on Sandbox Labs
- Mock Technical Interviews & Resume Auditing
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