AI Learning Ramp
A three-times-a-week ramp from GenAI query systems into AI infrastructure, agentic systems, evals, safety, and OpenAI or Anthropic style interviews.
Each new course page should fit one focused hour: a short reading set, a system-design frame, and one interview drill tied back to your BigQuery GenAI query-engine background. The first pass prioritizes AI engineering interview fundamentals before agent breadth.
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Prefill vs decode, KV cache, continuous batching, PagedAttention, and a BigQuery-style TTFT drill.
vLLM, TGI, TensorRT-LLM, and SGLang engine-selection tradeoffs with SLO and capacity planning.
TTFT, ITL, throughput, prompt caching, and admission-control tradeoffs for mixed interactive and agentic workloads.
Chunking, hybrid retrieval, reranking, freshness, and provenance for grounded answers and agent workflows.
Context selection, compaction, prompt caching, state boundaries, and long-context failure isolation for analytics agents.
The automation will add new course pages here as they are published.