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Infrastructure Strategy for AI Product Teams

7 chapters · 32 lessons

1. 1. The Infrastructure Layer of AI Products

   4 lessons

   Understand how compute, memory, and networking shape what AI products can deliver to users.

2. 2. Model Selection for Real-World Constraints

   5 lessons

   Choose the right model size and architecture based on latency, accuracy, and cost requirements.

3. 3. Designing Efficient Inference Architectures

   5 lessons

   Apply caching, batching, and retrieval patterns to reduce unnecessary compute and improve response time.

4. 4. Edge, Cloud, and Hybrid Deployment Patterns

   4 lessons

   Decide where workloads run based on privacy, latency, and cost considerations.

5. 5. Capacity Planning and Resource Allocation

   5 lessons

   Build models to forecast demand, allocate budget, and avoid infrastructure surprises.

6. 6. Communicating Infrastructure Tradeoffs

   4 lessons

   Translate technical constraints into product and business language for stakeholders.

7. 7. Optimizing Existing AI Products

   5 lessons

   Identify and resolve bottlenecks in live systems to improve performance and reduce cost.