Opening book details…
Can I read Practical MLOps : operationalizing machine learning models on EtoBox?
Practical MLOps : operationalizing machine learning models by Noah Gift; Alfredo Deza; Safari, an O'Reilly Media Company is a book available to read on EtoBox.
What is Practical MLOps : operationalizing machine learning models about?
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
- Author
- Noah Gift; Alfredo Deza; Safari, an O'Reilly Media Company
- Publisher
- O'Reilly Media, Incorporated
- Published
- 2021
- Language
- EN
- ISBN
- 9781098103019
- Subjects
- Computer Science, Science, Business
More by Noah Gift; Alfredo Deza; Safari, an O'Reilly Media Company
Browse all works by Noah Gift; Alfredo Deza; Safari, an O'Reilly Media Company
Similar books
- Practical MLOps — Noah Gift & Alfredo Deza (2021)
- 実践的MLOps — Alfredo Deza Noah Gift (2021)
- Machine Learning Engineering with Python : Manage the Lifecycle of Machine Learning Models Using MLOps with Practical Examples — Andrew P. McMahon (2023)
- Operationalizing Machine Learning Pipelines: Building Reusable and Reproducible Machine Learning Pipelines Using MLOps — Vishwajyoti Pandey; Shaleen Bengani (2022)
- Mastering MLOps Architecture: From Code to Deployment: Manage the Production Cycle of Continual Learning ML Models with MLOps — Raman Jhajj (2023)
- The Definitive Guide to Machine Learning Operations in AWS: Machine Learning Scalability and Optimization with AWS — Neel Sendas & Deepali Rajale (2025)