Google Cloud Generative AI - Self-Directed Study Track¶

ℹ️ Generative-AI-focused study track, not a discrete certification. Google announced the Generative AI Leader cert in late 2024 and continues to evolve its GenAI offerings. This dir is a focused study guide for GenAI on GCP (Vertex AI, Gemini, Imagen, MLOps for GenAI) that complements:

• ML cert coverage: exams/gcp/machine-learning-engineer/
• Architect coverage: exams/gcp/cloud-architect/ (PCA touches GenAI patterns)

Quick Links¶

• Fact Sheet - Exam logistics and blueprint
• Practice Plan - Comprehensive study roadmap

Study Materials¶

Core Notes¶

• Generative AI Fundamentals - PaLM, Gemini, foundation model concepts
• Vertex AI - Google Cloud's unified ML platform
• MLOps for Production GenAI - Production ML and GenAI workflows

Companion Materials¶

• GCP Professional Machine Learning Engineer - Deeper ML cert
• GCP Data Engineer - Data pipelines that feed ML models

Exam Focus Areas¶

The Professional ML Engineer exam covers:

1. Architecting Low-Code ML Solutions (12%)
2. AutoML for various data types
3. Pre-trained APIs and models

4. BigQuery ML for analytics

5. Collaborating Within and Across Teams (16%)

6. ML development workflows
7. Version control for ML artifacts

8. CI/CD for ML pipelines

9. Scaling Prototypes into ML Solutions (18%)

10. Model serving and deployment
11. A/B testing for ML models

12. Performance optimization

13. Serving and Scaling Models (20%)

14. Online and batch prediction
15. Model monitoring and management

16. Infrastructure scaling

17. Automating and Orchestrating ML Pipelines (18%)

18. Vertex AI Pipelines
19. Kubeflow Pipelines

20. Workflow orchestration

21. Monitoring ML Solutions (16%)

22. Model performance monitoring
23. Data drift detection
24. Logging and debugging

Key GCP ML Services¶

Vertex AI Platform¶

• Vertex AI Workbench: Jupyter-based development environment
• Vertex AI Training: Custom model training at scale
• Vertex AI Endpoints: Model serving infrastructure
• Vertex AI Pipelines: ML workflow orchestration
• Vertex AI Model Registry: Model versioning and management

AutoML Services¶

• AutoML Tables: Structured data ML
• AutoML Vision: Image classification and object detection
• AutoML Natural Language: Text classification and entity extraction
• AutoML Translation: Custom translation models
• AutoML Video Intelligence: Video analysis

Pre-trained APIs¶

• Vision API: Image analysis and OCR
• Natural Language API: Text analysis and sentiment
• Translation API: Language translation
• Speech-to-Text API: Audio transcription
• Text-to-Speech API: Voice synthesis
• Video Intelligence API: Video content analysis

Generative AI¶

• Vertex AI Generative AI Studio: Experiment with foundation models
• PaLM API: Large language model access
• Gemini Pro: Multimodal AI capabilities
• Model Garden: Pre-trained and foundation models

Supporting Services¶

• BigQuery ML: SQL-based machine learning
• Dataflow: Stream and batch data processing
• Dataproc: Managed Spark and Hadoop
• AI Platform Notebooks: Managed Jupyter notebooks
• TensorFlow Enterprise: Enterprise TensorFlow support

Prerequisites & Expectations¶

Required Knowledge¶

• 3+ years: Hands-on ML experience
• 1+ year: Google Cloud ML experience
• Programming: Python, SQL, and basic understanding of TensorFlow/scikit-learn
• Statistics: Understanding of statistical concepts and ML algorithms
• Data Engineering: Experience with data pipelines and preprocessing

Technical Skills¶

• Design and implement ML solutions on Google Cloud
• Build and operationalize ML pipelines
• Monitor and maintain production ML systems
• Apply ML best practices and responsible AI principles
• Optimize ML models for performance and cost

Success Criteria¶

• Design end-to-end ML solutions using Vertex AI
• Implement MLOps practices for production systems
• Choose appropriate ML algorithms and services for specific use cases
• Monitor and troubleshoot ML models in production
• Apply responsible AI practices and governance