Google Machine Learning and Generative AI for Solutions Architects

Table of Contents

1. AI/ML Concepts, Real-World Applications, and Challenges
2. Understanding the ML Model Development Lifecycle
3. AI/ML Tooling and the Google Cloud AI/ML Landscape
4. Utilizing Google Cloud's High-Level AI Services
5. Building Custom ML Models on Google Cloud
6. Diving Deeper—Preparing and Processing Data for AI/ML Workloads on Google Cloud
7. Feature Engineering and Dimensionality Reduction
8. Hyperparameters and Optimization
9. Neural Networks and Deep Learning
10. Deploying, Monitoring, and Scaling in Production
11. Machine Learning Engineering and MLOps with GCP
12. Bias, Explainability, Fairness, and Lineage
13. ML Governance and the Google Cloud Architecture Framework
14. Advanced Use Cases and Technologies
15. An Introduction to Generative AI
16. Generative AI on Google Cloud
17. Advanced Generative AI Concepts and Use Cases
18. Bringing It All Together—Building ML Solutions with GCP and Vertex