AZURE MACHINE LEARNING ENGINEERING : deploy, fine -tune and optimize ml models using microsoft azure
Description
More Details
Notes
Also in this Series
Reviews from GoodReads
Citations
Fakhraee, S., Balakreshnan, B., & Masanz, M. (2022). AZURE MACHINE LEARNING ENGINEERING: deploy, fine -tune and optimize ml models using microsoft azure . PACKT Publishing Limited.
Chicago / Turabian - Author Date Citation, 17th Edition (style guide)Fakhraee, Sina, Balamurugan. Balakreshnan and Megan. Masanz. 2022. AZURE MACHINE LEARNING ENGINEERING: Deploy, Fine -tune and Optimize Ml Models Using Microsoft Azure. [Place of publication not identified]: PACKT Publishing Limited.
Chicago / Turabian - Humanities (Notes and Bibliography) Citation, 17th Edition (style guide)Fakhraee, Sina, Balamurugan. Balakreshnan and Megan. Masanz. AZURE MACHINE LEARNING ENGINEERING: Deploy, Fine -tune and Optimize Ml Models Using Microsoft Azure [Place of publication not identified]: PACKT Publishing Limited, 2022.
Harvard Citation (style guide)Fakhraee, S., Balakreshnan, B. and Masanz, M. (2022). AZURE MACHINE LEARNING ENGINEERING: deploy, fine -tune and optimize ml models using microsoft azure. [Place of publication not identified]: PACKT Publishing Limited.
MLA Citation, 9th Edition (style guide)Fakhraee, Sina., Balamurugan Balakreshnan, and Megan Masanz. AZURE MACHINE LEARNING ENGINEERING: Deploy, Fine -tune and Optimize Ml Models Using Microsoft Azure PACKT Publishing Limited, 2022.
Staff View
Grouping Information
Grouped Work ID | 50bafaae-e19d-bb1e-8e8b-74e4f849d983-eng |
---|---|
Full title | azure machine learning engineering deploy fine tune and optimize ml models using microsoft azure |
Author | fakhraee sina |
Grouping Category | book |
Last Update | 2025-03-03 03:27:18AM |
Last Indexed | 2025-05-03 03:13:34AM |
Book Cover Information
Image Source | default |
---|---|
First Loaded | Apr 26, 2025 |
Last Used | Apr 26, 2025 |
Marc Record
First Detected | Dec 16, 2024 11:24:31 PM |
---|---|
Last File Modification Time | Dec 17, 2024 08:24:02 AM |
Suppressed | Record had no items |
MARC Record
LEADER | 04054cam a22004337 4500 | ||
---|---|---|---|
001 | on1359604044 | ||
003 | OCoLC | ||
005 | 20241217082148.0 | ||
006 | m o d | ||
007 | cr |n||||||||| | ||
008 | 230123s2022 xx o 000 0 eng d | ||
020 | |a 9781803241685|q (electronic bk.) | ||
020 | |a 1803241683|q (electronic bk.) | ||
035 | |a (OCoLC)1359604044 | ||
037 | |a 9781803239309|b O'Reilly Media | ||
037 | |a 10162224|b IEEE | ||
040 | |a YDX|b eng|c YDX|d ORMDA|d OCLCF|d IEEEE|d OCLCO | ||
049 | |a MAIN | ||
050 | 4 | |a Q325.5 | |
082 | 0 | 4 | |a 006.31|2 23/eng/20230124 |
100 | 1 | |a Fakhraee, Sina. | |
245 | 1 | 0 | |a AZURE MACHINE LEARNING ENGINEERING :|b deploy, fine -tune and optimize ml models using microsoft azure /|c Sina Fakhraee, Balamurugan Balakreshnan, Megan Masanz. |
260 | |a [Place of publication not identified]|b PACKT Publishing Limited,|c 2022. | ||
300 | |a 1 online resource | ||
336 | |a text|b txt|2 rdacontent | ||
337 | |a computer|b c|2 rdamedia | ||
338 | |a online resource|b cr|2 rdacarrier | ||
505 | 0 | |a Table of Contents Introducing Azure Machine Learning Working with Data in AMLS Training Machine Learning Models in AMLS Tuning Your Models with AMLS Azure Automated Machine Learning Deploying ML Models for Real-Time Inferencing Deploying ML Models for Batch Scoring Responsible AI Productionizing Your Workload with MLOps Using Deep Learning in Azure Machine Learning Using Distributed Training in AMLS. | |
520 | |a Fully build and productionize end-to-end machine learning solutions using Azure Machine Learning Service Key Features Automate complete machine learning solutions using Microsoft Azure Understand how to productionize machine learning models Get to grips with monitoring, MLOps, deep learning, distributed training, and reinforcement learning Book DescriptionData scientists working on productionizing machine learning (ML) workloads face a breadth of challenges at every step owing to the countless factors involved in getting ML models deployed and running. This book offers solutions to common issues, detailed explanations of essential concepts, and step-by-step instructions to productionize ML workloads using the Azure Machine Learning service. You'll see how data scientists and ML engineers working with Microsoft Azure can train and deploy ML models at scale by putting their knowledge to work with this practical guide. Throughout the book, you'll learn how to train, register, and productionize ML models by making use of the power of the Azure Machine Learning service. You'll get to grips with scoring models in real time and batch, explaining models to earn business trust, mitigating model bias, and developing solutions using an MLOps framework. By the end of this Azure Machine Learning book, you'll be ready to build and deploy end-to-end ML solutions into a production system using the Azure Machine Learning service for real-time scenarios. What you will learn Train ML models in the Azure Machine Learning service Build end-to-end ML pipelines Host ML models on real-time scoring endpoints Mitigate bias in ML models Get the hang of using an MLOps framework to productionize models Simplify ML model explainability using the Azure Machine Learning service and Azure Interpret Who this book is for Machine learning engineers and data scientists who want to move to ML engineering roles will find this AMLS book useful. Familiarity with the Azure ecosystem will assist with understanding the concepts covered. | ||
590 | |a O'Reilly|b O'Reilly Online Learning: Academic/Public Library Edition | ||
630 | 0 | 0 | |a Microsoft Azure (Computing platform) |
650 | 0 | |a Machine learning.|9 46043 | |
700 | 1 | |a Balakreshnan, Balamurugan. | |
700 | 1 | |a Masanz, Megan. | |
776 | 0 | 8 | |i Print version:|z 1803239301|z 9781803239309|w (OCoLC)1352963231 |
856 | 4 | 0 | |u https://library.access.arlingtonva.us/login?url=https://learning.oreilly.com/library/view/~/9781803239309/?ar|x O'Reilly|z eBook |
938 | |a YBP Library Services|b YANK|n 19089264 | ||
994 | |a 92|b VIA | ||
999 | |c 358786|d 358786 |