Machine learning models are powerful — but getting them from a Jupyter notebook to a reliable production environment can be a daunting journey. That’s why I wrote the MLOps Field Manual.
This book distills years of experience into a concise, actionable guide for delivering machine learning systems that are not only accurate, but also maintainable, scalable, and trustworthy.
Inside, you’ll find:
- Clear explanations of the ML lifecycle, from data collection to post-deployment monitoring.
- Proven best practices for reproducibility, versioning, and CI/CD in machine learning.
- Deployment strategies like blue-green, canary, A/B testing, and shadow deployments.
- Troubleshooting guides for common production problems.
- Tool-agnostic advice so you can adapt the concepts to your stack.
Whether you’re a data scientist learning production workflows, an ML engineer building reliable pipelines, or a developer breaking into AI operations, the MLOps Field Manual gives you the patterns, strategies, and confidence to make your models production-ready.
It’s more than just a book — it’s a compass for navigating the fast-moving world of machine learning operations.
📖 Grab your copy now and start building ML systems that perform today — and still perform tomorrow.
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