Best production systems book: AI Engineering. Chip Huyen's resource for building reliable AI applications. Start here when a demo needs to become a system.
Best lifecycle course: Full Stack Deep Learning Lectures. Full Stack Deep Learning course videos on the ML and AI product lifecycle. Use it for deployment, iteration, product quality, and operating concerns.
Best tracing and eval tooling: Phoenix by Arize. Open-source observability and evaluation tooling. Use it when you need to see why an AI workflow failed.
Production AI is where demos meet constraints
A demo can ignore latency, cost, monitoring, user feedback, model upgrades, privacy, retries, and failure handling. Production AI cannot. The best resources teach the operating system around the model.
Chip Huyen is the strongest production AI starting point. Full Stack Deep Learning gives lifecycle context. Phoenix and Langfuse help when you need observability and evals in a running system. Pair those with current provider docs on batch processing, prompt caching, flex modes, and retrieval so your production choices match how the APIs actually behave now.
Build one measured feature
A good learning project should include logs, eval examples, model comparison, cost measurement, latency measurement, and a written list of failure modes. Without those pieces, it is still mostly a demo.
Avoid resources that imply production is just deployment. The hard part is knowing whether the system is good, whether it is improving, and what happens when it is wrong.
Recommended courses and resources
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AI Engineering
Book · Chip Huyen · Intermediate to advanced
You are moving from demos to production systems.
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Building and Evaluating Advanced RAG Applications
Short course · DeepLearning.AI · Intermediate
You already know basic RAG and need better retrieval, evaluation, and production-quality patterns.
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Qwen Code September 10 weekly update
Coding agent workflow release notes · Qwen · Intermediate to advanced
You want Qwen Code's September 10, 2026 update on workflow run history, agent and token observability, context-usage inspection, combining Plan with YOLO mode, and named parallel channel tasks in isolated workspaces.
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How GitHub makes AI coding more cost efficient
Coding agent evaluation guide · GitHub · Intermediate to advanced
You want GitHub's September 2, 2026 evidence for measuring coding-agent efficiency across the whole task, including selective output compression, preserving useful context, benchmark regressions, and controlled production experiments.
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Anthropic ant apply
Agent infrastructure guide · Anthropic · Intermediate to advanced
You want Anthropic's September 3, 2026 workflow for declaring agents, environments, skills, memory stores, and scheduled deployments in a repository, previewing changes, and applying them reproducibly in CI.
