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From foundations to production: training, evaluation, drift, and the math that actually matters.
Retrieval, ranking, embeddings, candidate generation — how the feeds you love are built.
Patterns, tradeoffs and real architectures for high-throughput, low-latency systems.
Azure, AWS, and GCP — service deep-dives, cost optimization, and battle-tested topologies.
Spark, Kafka, lakehouses and petabyte pipelines. Build data that engineers can trust.
Tool-use, planning, memory and the operating systems for autonomous LLM workflows.