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Designing Reusable AI Systems with Foundation Models

7 chapters · 33 lessons

1. 1. Foundation Model Architecture and Economics

   4 lessons

   Understand when foundation models deliver value and how they differ from task-specific approaches.

2. 2. Learned Representations and Embeddings

   5 lessons

   Master how foundation models encode knowledge as reusable numerical representations.

3. 3. Model Adaptation Strategies

   5 lessons

   Learn four core methods to adapt foundation models to specific downstream tasks.

4. 4. Building RAG Systems with Vector Databases

   5 lessons

   Design retrieval-augmented systems that connect foundation models to live knowledge bases.

5. 5. Multi-Task Evaluation and Benchmarking

   4 lessons

   Build evaluation frameworks that measure foundation model performance across diverse tasks.

6. 6. Production System Design and Deployment

   5 lessons

   Architect scalable systems that serve foundation models across multiple workflows.

7. 7. Building Domain-Specific Foundation Models

   5 lessons

   Learn when and how to train your own foundation model for specialized domains.