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AI Inference Deployment: Cloud, Edge, and Hybrid Architecture

7 chapters · 35 lessons

1. 1. Inference Fundamentals and Performance Characteristics

   5 lessons

   Understand how inference differs from training and identify the key performance metrics that matter in production.

2. 2. Model Optimization Techniques for Production

   5 lessons

   Apply quantization, pruning, and compression methods to reduce model size and improve inference speed.

3. 3. Cloud Inference Architecture and Scaling

   5 lessons

   Design cloud-based inference systems that handle variable load, optimize cost, and maintain service level objectives.

4. 4. Edge and Local Inference Deployment

   5 lessons

   Deploy models to resource-constrained devices while managing power, memory, and thermal limitations.

5. 5. Hybrid Inference Systems and Routing Logic

   5 lessons

   Build systems that intelligently route inference requests between cloud and edge based on workload characteristics.

6. 6. Advanced Optimization and Serving Patterns

   5 lessons

   Implement retrieval augmented generation, speculative decoding, and other techniques that improve inference efficiency.

7. 7. Production Monitoring and Continuous Optimization

   5 lessons

   Establish observability, performance benchmarking, and iterative improvement processes for inference systems.