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Practical MLOps with Python : A Beginner's Guide to Building, Deploying, and Maintaining Production-Ready Machine Learning Pipelines
Are you a data scientist or developer who can build machine learning models but struggles to deploy them effectively? Do you find the leap from a Jupyter Notebook to a live, production system daunting?
If so, you're not alone. The world of Machine Learning Operations (MLOps) is the missing link for many, and Practical MLOps with Python is your definitive guide to bridging that gap.
This book demystifies the entire MLOps lifecycle, providing a hands-on, practical approach to automating and managing machine learning pipelines.
Inside, you will Core MLOps Understand the fundamental concepts and how they differ from traditional DevOps.End-to-End Project Follow a complete project from data ingestion and model training to deployment and monitoring.Modern Get hands-on experience with industry-standard tools like Docker, Kubernetes, Airflow, and cloud platforms (AWS, GCP, Azure).Automated Learn to build robust, automated CI/CD pipelines for your models using TensorFlow, PyTorch, and Scikit-learn.Monitoring and Master the techniques for monitoring models in production, detecting data drift, and triggering retraining.Real-World Case Explore practical examples and case studies that you can adapt for your own projects.Whether you are a student, a junior data scientist, or an experienced developer looking to skill up, this book provides the practical knowledge you need to become a proficient MLOps practitioner.
Stop letting your models die in development. Start building scalable, production-ready ML systems today. Scroll up and grab your copy now!
If so, you're not alone. The world of Machine Learning Operations (MLOps) is the missing link for many, and Practical MLOps with Python is your definitive guide to bridging that gap.
This book demystifies the entire MLOps lifecycle, providing a hands-on, practical approach to automating and managing machine learning pipelines.
Inside, you will Core MLOps Understand the fundamental concepts and how they differ from traditional DevOps.End-to-End Project Follow a complete project from data ingestion and model training to deployment and monitoring.Modern Get hands-on experience with industry-standard tools like Docker, Kubernetes, Airflow, and cloud platforms (AWS, GCP, Azure).Automated Learn to build robust, automated CI/CD pipelines for your models using TensorFlow, PyTorch, and Scikit-learn.Monitoring and Master the techniques for monitoring models in production, detecting data drift, and triggering retraining.Real-World Case Explore practical examples and case studies that you can adapt for your own projects.Whether you are a student, a junior data scientist, or an experienced developer looking to skill up, this book provides the practical knowledge you need to become a proficient MLOps practitioner.
Stop letting your models die in development. Start building scalable, production-ready ML systems today. Scroll up and grab your copy now!
132 pages, Kindle Edition
Published July 26, 2025
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