Design a distillation pipeline that improves a target task without hiding capability regressions

Version teacher outputs, separate learning from evaluation, and catch a release whose domain score improves while a protected capability regresses.

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Version teacher outputs, separate learning from evaluation, and catch a release whose domain score improves while a protected capability regresses.

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🧮 Open Weights & Serving Engines🔒 Premium

Pretraining, Fine-Tuning and DistillationUnderstand how a base model becomes a useful application model. Separate training objectives from LoRA and quantization, compare distillation with retrieval, and choose the smallest intervention supported by your evaluation.

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📐 AI Systems Design🔒 Premium

Evaluation and Data Pipeline InfrastructureData pipelines prepare traceable training inputs; evaluation pipelines compare model behavior. Both need versioned inputs, reproducible execution and useful failure reporting.

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🧮 Open Weights & Serving Engines

Serving Benchmarks That Do Not LieDesign reproducible serving experiments with realistic arrivals, cache state and failure accounting. Interpret latency-qualified throughput and conditional per-rank bounds without turning optimistic kernel estimates into production capacity.

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🩺 Fleet Reliability & Observability🔒 Premium

Evaluation Metrics and Data SplitsFast predictions can still be wrong. Calculate precision and recall from explicit outcomes, choose thresholds on held-out data, and keep quality evidence separate from serving latency and availability.

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