Google Cloud Professional Machine Learning Engineer Cert Prep: 1 Framing ML Problems

Introduction

• Course and Google Professional Machine Learning Engineer exam overview
• Course 1 key terminology

1. Translating Business Challenges into ML Use Cases

• Building AI-enabled workflows
• Using AI tools to build AI tools
• Teaching MLOps at scale with GitHub

2. Defining ML Problems

• Simulations vs. experiment tracking
• When to use ML
• Supervised vs. unsupervised ML
• Optimization
• Clustering

3. Defining Business Success Criteria

• Defining business success criteria

4. Identifying Risks to Feasibility of ML Solutions

• MLOps hierachy of needs
• Hidden costs of bespoke systems
• Data poisoning

Conclusion

• Next steps