Description
BPB Publications Machine Learning in Production by Suhas Pote
Deploy, manage, and scale Machine Learning models with MLOps effortlesslyDescription‘Machine Learning in Production’ is an attempt to decipher the path to a remarkable career in the field of MLOps. It is a comprehensive guide to managing the machine learning lifecycle from development to deployment, outlining ways in which you can deploy ML models in production.It starts off with fundamental concepts, an introduction to the ML lifecycle and MLOps, followed by comprehensive step-by-step instructions on how to develop a package for ML code from scratch that can be installed using pip. It then covers MLflow for ML life cycle management, CI/CD pipelines, and shows how to deploy ML applications on Azure, GCP, and AWS. Furthermore, it provides guidance on how to convert Python applications into Android and Windows apps, as well as how to develop ML web apps. Finally, it covers monitoring, the critical topic of machine learning attacks, and A/B testing.What you will learn● Master the Machine Learning lifecycle with MLOps.● Streamline your ML workflow with MLFlow.● Use Docker and Kubernetes for ML deployment.Who this book is forWhether you are a Data scientist, ML engineer, DevOps professional, Software engineer, or Cloud architect, this book will help you get your machine learning models into production quickly and efficiently.Table of Contents1. Python 1012. Git and GitHub Fundamentals3. Challenges in ML Model Deployment4. Packaging ML Models5. MLflow-Platform to Manage the ML Life Cycle6. Docker for ML7. Build ML Web Apps Using API8. Build Native ML Apps9. CI/CD for ML10. Deploying ML Models on Heroku11. Deploying ML Models on Microsoft Azure12. Deploying ML Models on Google Cloud Platform13. Deploying ML Models on Amazon Web Services14. Monitoring and Debugging15. Post-Productionizing ML Models