Practical mlops operationalizing machine von gift noah (9 Ergebnisse)

Paperback. Zustand: Very Good. 1. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.

Zustand: Good. Item in good condition. Textbooks may not include supplemental items i.e. CDs, access codes etc.

Zustand: Very Good. Item in very good condition! Textbooks may not include supplemental items i.e. CDs, access codes etc.

Paperback. Zustand: Very Good. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.

Zustand: New. In English.

Zustand: New. 2021. Paperback. . . . . . Books ship from the US and Ireland.

Zustand: New.

Paperback. Zustand: Brand New. 450 pages. 9.19x7.00x0.93 inches. In Stock.

Taschenbuch. Zustand: Neu. Neuware - 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.…