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.
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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Llama API models
Model docs · Meta Llama · Beginner to advanced
You need the current official Llama model catalog, capability summaries, and API access route before choosing hosted or local deployment.
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Qwen API platform
API docs · Qwen · Beginner to advanced
You need official Qwen model-family context, deployment docs, and quickstarts before choosing a hosted or local workflow.
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Llama Cookbook
GitHub repo · Meta Llama · Beginner to advanced
You want Meta's practical recipes for inference, fine-tuning, RAG, and end-to-end Llama applications.
