Full course coming soon

AI Accelerator Selection and Deployment for Production Systems

7 chapters · 35 lessons

1. 1. Inference Workload Characteristics and Requirements

   5 lessons

   Learn to profile and characterize your AI workload to establish hardware selection criteria.

2. 2. Accelerator Architecture Fundamentals

   5 lessons

   Understand how chip design choices affect inference performance for different model types.

3. 3. Benchmarking and Performance Validation

   5 lessons

   Design experiments that reveal real-world accelerator performance for your specific use case.

4. 4. Total Cost of Ownership Analysis

   5 lessons

   Calculate the true cost of accelerator options across acquisition, operation, and scaling.

5. 5. Software Stack and Ecosystem Considerations

   5 lessons

   Evaluate the compiler, runtime, and tooling maturity that determines operational success.

6. 6. Deployment Patterns and Infrastructure Design

   5 lessons

   Design serving infrastructure that matches your scale, latency, and reliability requirements.

7. 7. Decision Frameworks and Vendor Evaluation

   5 lessons

   Build a structured process for making and defending accelerator selection decisions.