Full course coming soon

Machine Learning System Design for Product and Engineering Teams

7 chapters · 33 lessons

1. 1. Framing Problems for Machine Learning

   4 lessons

   Learn when machine learning is the right tool and how to translate business requirements into ML task definitions.

2. 2. Data Strategy and Pipeline Design

   5 lessons

   Build the data foundations that determine model quality, from collection through labeling and versioning.

3. 3. Model Training and Selection

   5 lessons

   Understand the training loop, loss functions, and how to choose model complexity for your constraints.

4. 4. Evaluation Beyond Accuracy

   5 lessons

   Design evaluation strategies that measure what matters in production, including fairness, robustness, and edge cases.

5. 5. Deployment and Integration Patterns

   5 lessons

   Learn deployment architectures, rollout strategies, and how to integrate ML predictions into product workflows.

6. 6. Monitoring and Model Maintenance

   5 lessons

   Build systems to detect model degradation, data drift, and performance issues before they impact users.

7. 7. Cross-Functional Collaboration and Communication

   4 lessons

   Develop the vocabulary and frameworks to align stakeholders, manage expectations, and ship responsibly.