Exam Ref Dp-900 Microsoft Azure Data Fundamentals

Exam Ref Dp-900 Microsoft Azure Data Fundamentals

Author: Daniel Seara

Publisher: Microsoft Press

ISBN: 0137252161


Page: 336

View: 185

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Direct from Microsoft, this Exam Ref is the official study guide for the new Microsoft DP-900 Microsoft Azure Data Fundamentals certification exam. Exam Ref DP-900 Microsoft Azure Data Fundamentals offers professional-level preparation that helps candidates maximize their exam performance and sharpen their skills on the job. It focuses on the specific areas of expertise modern IT professionals need to demonstrate real-world foundational knowledge of core data concepts and how they are implemented using Microsoft Azure data services. Coverage includes: Describing core data concepts Describing how to work with relational data on Azure Describing how to work with non-relational data on Azure Describing analytics workloads on Azure Microsoft Exam Ref publications stand apart from third-party study guides because they: Provide guidance from Microsoft, the creator of Microsoft certification exams Target professional-level exam candidates with content focused on their needs, not "one-size-fits-all" content Streamline study by organizing material according to the exam's objective domain (OD), covering one functional group and its objectives in each chapter Feature Thought Experiments to guide candidates through a set of "what if?" scenarios, and prepare them more effectively for Pro-level style exam questions Explore big picture thinking around the professional's job role For more information on Exam DP-900 and the Microsoft Certified: Azure Data Fundamentals credential, visit https: //docs.microsoft.com/en-us/learn/certifications/exams/DP-900.

Practical MLOps

Practical MLOps

Author: Noah Gift

Publisher: "O'Reilly Media, Inc."

ISBN: 9781098102968

Category: Computers

Page: 460

View: 722

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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