Machine Learning in Production

Аннотация

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.