The MLOps Recipe Cookbook
This cookbook is a practical guide to building production-ready AI systems using open-source MLOps tools. We’ve borrowed the language of gastronomy because it captures something important about how well-engineered AI systems are actually built: preparation matters, experimentation is normal, and small changes can lead to deliciously effective outcomes, if everything is cooked properly.
If you’re an ML engineer, data scientist or CTO trying to move from prototype to production, this book shows you what that journey actually looks like, in repeatable recipes you can run, adapt, and serve time and time again.
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What's inside
This first edition is an amuse-bouche: six carefully prepared recipes that show how MLOps works on real systems.
Each recipe focuses on a specific AI use case:
- Training and tracking experiments in computer vision
- Running models on constrained edge hardware
- Building and governing a self-hosted LLM agent
- Designing, evaluating, and productionising a RAG system
Across them, you’ll move through the core stages of modern MLOps: data version control, experiment tracking, training pipelines, model serving, and monitoring in production.
You can run each recipe as it is to understand the fundamentals, or adapt it to your own use case.
Every recipe links to the corresponding GitHub repository, so you can move between explanation and execution. Read a step, check the code, run it, then season to taste.
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