Advanced · every module free · 7 challenges
Requirements, metrics, architecture and tradeoffs — the framework behind every design interview and every real system.
Data, tensor and pipeline parallelism, ZeRO and FSDP, and making a run survive a failed node.
The memory hierarchy, arithmetic intensity, fused kernels and writing your own in Triton.
Quantization, batching, KV cache management, speculative decoding and the latency budget behind them.
Feature stores, streaming versus batch, point-in-time correctness and lineage.
Drift detection, shadow deploys, canaries, rollback and the on-call reality of owning a model.
Unit economics, build versus buy, and making the case for an ML investment to people who do not build models.