Full course coming soon

Workload Placement for Distributed AI Systems

7 chapters · 31 lessons

1. 1. Constraint-Based Architecture for Inference Placement

   4 lessons

   Learn to map application requirements to infrastructure tiers by analyzing latency, privacy, connectivity, and cost constraints.

2. 2. Model Compression for Resource-Constrained Devices

   5 lessons

   Apply quantization, pruning, and distillation techniques to reduce model size and inference cost while preserving accuracy.

3. 3. Heterogeneous Hardware and Accelerator Utilization

   5 lessons

   Optimize inference across CPUs, GPUs, DSPs, and neural accelerators by understanding processor capabilities and scheduling strategies.

4. 4. Dynamic Workload Routing and Hybrid Architectures

   4 lessons

   Build systems that route inference requests intelligently based on runtime conditions like battery state, network quality, and device load.

5. 5. Local Retrieval and On-Device Data Management

   4 lessons

   Implement retrieval-augmented generation and vector search on edge devices to support private, low-latency context retrieval.

6. 6. Selective Sync and Telemetry Design

   4 lessons

   Design data pipelines that minimize bandwidth and preserve privacy by syncing only essential results, logs, and feedback.

7. 7. Deployment Patterns and Real-World Case Studies

   5 lessons

   Apply workload placement strategies to manufacturing, healthcare, retail, and mobile use cases with concrete deployment patterns.