Full course coming soon

Building Machine Learning Infrastructure for Product Teams

7 chapters · 33 lessons

1. 1. Platform Architecture and Design Principles

   4 lessons

   Understand the core components of ML platforms and how they compose into reusable infrastructure.

2. 2. Feature Engineering and Feature Stores

   5 lessons

   Build reusable feature pipelines that turn raw events into consistent model inputs.

3. 3. Model Training and Experiment Management

   5 lessons

   Standardize how teams train, evaluate, and version models across the organization.

4. 4. Deployment Pipelines and Inference Serving

   5 lessons

   Build automated workflows that move models from training to production safely and efficiently.

5. 5. Monitoring, Observability, and Incident Response

   5 lessons

   Detect and diagnose model degradation, data drift, and system failures in production.

6. 6. Governance, Compliance, and Model Risk Management

   5 lessons

   Implement controls for data lineage, model explainability, and regulatory requirements.

7. 7. Platform Adoption and Team Enablement

   4 lessons

   Drive platform usage across product teams through documentation, support, and iterative improvement.