Free Lesson
🚀 Launch a Model: Evals, Inference, and Deployment
Part of Life of a Model: Build, Teach, Tune, Ship
60 min
Nov 11, 2026 2:00 PM
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What you'll learn
Design evals that prove a model is good
Combine benchmarks, custom evals, LLM-as-judge, and human review to measure what matters for your use case.
Understand inference: prefill, decode, and KV cache
See where latency and cost come from, and how batching and hardware choices change throughput.
Deploy with guardrails, monitoring, and a feedback loop
Ship through APIs, watch production behavior, and feed new data into the next cycle.
Why this topic matters
A model isn't done when training ends. Evals tell you if it's good, and inference and deployment decide whether anyone can actually use it. Latency, cost per token, and reliability make or break an AI product in the real world. This lesson closes the loop: production feedback becomes the data that starts the next life of the model.
