Machine Learning in Production
Turn your machine learning models into real, production-ready systems with a practical guide that cuts through the noise and shows you exactly how to build, deploy, and scale ML applications step by step.
Machine learning doesn't deliver value until it runs reliably in production and this book gives you the complete roadmap. Machine Learning in Production is a hands-on guide designed for developers, data scientists, and engineers who want to move beyond experiments and build real-world ML systems that users depend on.
Through clear explanations, practical examples, and modern best practices, you'll learn how to train models with Python, deploy scalable APIs using FastAPI, containerize applications with Docker, and manage the full lifecycle of your ML projects with MLflow. The book walks through everything from feature engineering and testing ML APIs to monitoring, automation, CI/CD, cloud deployment, and security.
Whether you're working on your first ML deployment or refining an existing pipeline, this book provides an end-to-end blueprint for building robust, efficient, and maintainable machine learning systems that perform at scale.
Key Features:
- Build production-ready ML pipelines with Python, FastAPI, Docker, and MLflow.
- Learn modern MLOps workflows, including CI/CD, experiment tracking, and automated retraining.
- Deploy scalable APIs and microservices optimized for high availability and low latency
- Implement monitoring, logging, drift detection, and real-time performance tracking.
- Gain practical guidance on security, versioning, testing, and cloud infrastructure.
Ready to deploy machine learning like a professional?
Start your journey today with Machine Learning in Production and build ML systems that are fast, reliable, and built to scale.
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