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Infrastructure Planning for AI Applications

7 chapters · 32 lessons

1. 1. Memory Requirements for AI Workloads

   5 lessons

   Learn how to calculate and estimate memory needs for different AI applications and model types.

2. 2. Compute and Memory Trade-offs

   4 lessons

   Understand when compute speed matters versus when memory capacity becomes the limiting factor.

3. 3. Model Optimization for Memory Constraints

   5 lessons

   Apply quantization, pruning, and compression techniques to fit models within available memory.

4. 4. Workload Profiling and Bottleneck Analysis

   4 lessons

   Use profiling tools and metrics to identify where your AI system is constrained.

5. 5. Infrastructure Specification and Hardware Selection

   5 lessons

   Translate workload requirements into concrete hardware and deployment specifications.

6. 6. Optimizing Data Movement and Storage

   4 lessons

   Design storage and caching strategies that minimize latency and maximize throughput.

7. 7. Scaling Infrastructure with Product Growth

   5 lessons

   Plan infrastructure evolution as workloads grow from prototype to production scale.