Inference at Scale
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Batching strategies, KV caching, continuous batching, and the economics of serving large models.
Pillar 02
System Design
From single-GPU prototypes to multi-region inference clusters — the operational patterns that make ML work at scale.
Inference at Scale
Vector Database Internals
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HNSW indexing, product quantization, approximate nearest neighbour search, and when to reach for FAISS vs Pinecone.
Distributed Training Patterns
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Data, tensor, and pipeline parallelism — choosing the right strategy for your model size and cluster topology.
Feature Stores & Online Serving
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Point-in-time correctness, low-latency retrieval, and bridging the offline/online gap.