MLOps Fundamentals: Deploying and Managing Machine Learning Models in Production

Unlock the power of machine learning in production environments.

MLOps Fundamentals is your comprehensive guide to deploying, managing, and scaling machine learning models in production. Whether you’re an aspiring MLOps engineer, data scientist, or software developer, this book equips you with the foundational knowledge and practical tools to move machine learning models from experimentation to real-world deployment.

You’ll learn how to build end-to-end machine learning pipelines, automate workflows, and monitor model performance in production. From model training and testing to versioning and deployment, MLOps Fundamentals covers it all—ensuring your models run smoothly, efficiently, and securely.

Inside, you’ll discover how to:

• Set up a complete MLOps workflow using tools like Docker, Kubernetes, and CI/CD

• Automate model training, testing, and deployment with MLFlow and Kubeflow

• Version and manage models using tools like DVC and ModelDB

• Build robust pipelines that handle data preprocessing, training, and deployment

• Monitor and manage deployed models for performance, accuracy, and drift

• Scale machine learning models with cloud platforms like AWS, Google Cloud, and Azure

• Implement model rollback, A/B testing, and continuous integration strategies

• Ensure security and governance in an MLOps environment

• Collaborate with teams effectively using best practices in MLOps culture

With hands-on examples, code snippets, and real-world scenarios, this book helps you apply MLOps principles to make machine learning models production-ready and scalable.

Whether you’re deploying models for web apps, customer insights, or predictive maintenance, MLOps Fundamentals provides the knowledge and tools to bring your AI models to life in production.