Machine Learning in Production: Master the art of delivering robust Machine Learning solutions with MLOps
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Machine Learning in Production: Master the art of delivering robust Machine Learning solutions with MLOps

Machine Learning in Production: Master the art of delivering robust Machine Learning solutions with MLOps


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About the Book

Deploy, manage, and scale Machine Learning models with MLOps effortlessly Description ‘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 for Whether 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 Contents 1. Python 101 2. Git and GitHub Fundamentals 3. Challenges in ML Model Deployment 4. Packaging ML Models 5. MLflow-Platform to Manage the ML Life Cycle 6. Docker for ML 7. Build ML Web Apps Using API 8. Build Native ML Apps 9. CI/CD for ML 10. Deploying ML Models on Heroku 11. Deploying ML Models on Microsoft Azure 12. Deploying ML Models on Google Cloud Platform 13. Deploying ML Models on Amazon Web Services 14. Monitoring and Debugging 15. Post-Productionizing ML Models


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Product Details
  • ISBN-13: 9789355518101
  • Publisher: BPB Publications
  • Binding: Paperback
  • Language: English
  • Weight: 26.416 gr
  • ISBN-10: 9355518102
  • Publisher Date: 29 Apr 2023
  • Height: 234.95 mm
  • No of Pages: 458


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Machine Learning in Production: Master the art of delivering robust Machine Learning solutions with MLOps
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