Borrow Practical Mlops Operationalizing Machine Learning Models
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Practical MLOps
Getting your models into production is the fundamental challenge of machine learning. MLOps offers a set of proven principles aimed at solving this problem in a reliable and automated way. This insightful guide takes you through what MLOps is (and how it differs from DevOps) and shows you how to put it into practice to operationalize your machine learning models. Current and aspiring machine learning engineers--or anyone familiar with data science and Python--will build a foundation in MLOps tools and methods (along with AutoML and monitoring and logging), then learn how to implement them in AWS, Microsoft Azure, and Google Cloud. The faster you deliver a machine learning system that works, the faster you can focus on the business problems you're trying to crack. This book gives you a head start. You'll discover how to: Apply DevOps best practices to machine learning Build production machine learning systems and maintain them Monitor, instrument, load-test, and operationalize machine learning systems Choose the correct MLOps tools for a given machine learning task Run machine learning models on a variety of platforms and devices, including mobile phones and specialized hardware
Practical MLOps
Practical MLOps: Operationalizing Machine Learning Models is a hands-on guide for data scientists, machine learning engineers, and DevOps practitioners looking to take their machine learning models from the research phase into production. This book provides practical, actionable insights into the entire machine learning lifecycle, from model development to deployment, monitoring, and continuous improvement. Through real-world examples and step-by-step instructions, you will learn how to integrate MLOps practices into your workflow, automating the model deployment process, building scalable pipelines, and ensuring seamless collaboration across cross-functional teams. Covering essential topics such as model versioning, data management, experiment tracking, and performance monitoring, this book emphasizes the importance of robust, repeatable processes in managing the operational aspects of machine learning. You will also explore key MLOps tools and frameworks like Kubernetes, Docker, TensorFlow, and MLflow, and how to use them to streamline model deployment and scaling. Whether you're new to MLOps or looking to refine your existing practices, Practical MLOps provides a comprehensive roadmap to mastering the complexities of bringing machine learning models to production in a sustainable and reliable way. Key Features: In-depth coverage of MLOps principles and practices Step-by-step guides to automating and managing ML workflows Real-world examples using popular tools and frameworks Best practices for model deployment, scaling, and monitoring Insights into collaboration between data scientists, engineers, and business teams Practical MLOps is the definitive guide for transforming your machine learning models into production-ready, business-impacting solutions.
Microsoft Azure Essentials Azure Machine Learning
Microsoft Azure Essentials from Microsoft Press is a series of free ebooks designed to help you advance your technical skills with Microsoft Azure. This third ebook in the series introduces Microsoft Azure Machine Learning, a service that a developer can use to build predictive analytics models (using training datasets from a variety of data sources) and then easily deploy those models for consumption as cloud web services. The ebook presents an overview of modern data science theory and principles, the associated workflow, and then covers some of the more common machine learning algorithms in use today. It builds a variety of predictive analytics models using real world data, evaluates several different machine learning algorithms and modeling strategies, and then deploys the finished models as machine learning web services on Azure within a matter of minutes. The ebook also expands on a working Azure Machine Learning predictive model example to explore the types of client and server applications you can create to consume Azure Machine Learning web services. Watch Microsoft Press’s blog and Twitter (@MicrosoftPress) to learn about other free ebooks in the Microsoft Azure Essentials series.