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

BPB Publications, 2023. — 469 p.Deploy, manage, and scale Machine Learning models with MLOps effortlessly.Key Features.Explore several ways to build and deploy ML models in production using an automated CI/CD pipeline.
Develop and convert ML apps into Android and Windows apps.
Learn how to implement ML model deployment on popular cloud platforms, including Azure, GCP, and AWS.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 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, and CI/Cand D pipelines, and shows how to deploy ML applications on Azure, GCP, and AWS. Furthermore, it guides 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.Productionizing the Machine Learning model is a complex task that requires a comprehensive understanding of the latest technologies and CI/CD pipeline. MLOps become increasingly popular in the field of Data Science. This book is designed to provide a comprehensive guide to building and deploying ML applications with MLOps. It covers a wide range of topics, including the basics of Python programming, Git, Machine Learning life cycle, Docker, and advanced concepts such as packaging Python code for ML models, monitoring, model security, Kubernetes, testing using Pytest and the use of CI/CD pipeline for building and deploying robust and scalable ML applications on cloud platforms, including Azure, GCP, and AWS.Throughout the book, you will learn about the MLOps, various tools, and techniques to deploy ML models. You will also learn how to use them to produce ML models and applications that are efficient, scalable, and easy to maintain. Additionally, you will learn about best practices and design patterns for MLOps.With this book, you can easily build and deploy machine learning solutions in production.What you will learn.Master the Machine Learning lifecycle with MLOps.
Learn best practices for managing ML models at scale.
Streamline your ML workflow with MLFlow.
Implement monitoring solutions using Whylogs, WhyLabs, Grafana, and Prometheus.
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.Python 101.
Git and GitHub Fundamentals.
Challenges in ML Model Deployment.
Packaging ML Models.
MLflow-Platform to Manage the ML Life Cycle.
Docker for ML.
Build ML Web Apps Using API.
Build Native ML Apps.
CI/CD for ML.
Deploying ML Models on Heroku.
Deploying ML Models on Microsoft Azure.
Deploying ML Models on Google Cloud Platform.
Deploying ML Models on Amazon Web Services.
Monitoring and Debugging.
Post-Productionizing ML Models.