Azure Machine Learning Engineering: Deploy, fine-tune, and optimize ML models using Microsoft Azure
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Fully build and productionize end-to-end machine learning solutions using Azure Machine Learning Service
Key Features:
Automate complete machine learning solutions using Microsoft AzureUnderstand how to productionize machine learning modelsGet to grips with monitoring, MLOps, deep learning, distributed training, and reinforcement learning
Book Description:
Data 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 serviceBuild end-to-end ML pipelinesHost ML models on real-time scoring endpointsMitigate bias in ML modelsGet the hang of using an MLOps framework to productionize modelsSimplify 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.
Publisher : Packt Publishing (January 20, 2023)
Language : English
Paperback : 362 pages
ISBN-10 : 1803239301
ISBN-13 : 978-1803239309
Item Weight : 1.39 pounds
Dimensions : 9.25 x 7.52 x 0.75 inches
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8 reviews for Azure Machine Learning Engineering: Deploy, fine-tune, and optimize ML models using Microsoft Azure
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MS3 –
Excellent Guide with Clear Instructions and Practical Examples
This book on Azure Machine Learning Engineering is a fantastic read. The instructions are simple and easy to follow, making complex concepts accessible even to those new to the field. The examples and walkthroughs are particularly helpful, providing practical, hands-on experience that reinforces the material.Each chapter is well-structured, guiding you through deploying, fine-tuning, and optimizing ML models with Microsoft tools. The clear explanations and step-by-step approach make it an invaluable resource for both beginners and experienced practitioners looking to deepen their understanding of Azure ML.Overall, this book is a great investment for anyone interested in mastering machine learning with Azure. Highly recommended!
Voke –
Fantastic Book
Straight to the point and gets you where you want to go without any bumps.
Zach Grimaldi –
Thorough, Helpful, Current
Up-skill quickly and build working models in your first sit down at your desk. You can copy the source code for the entire book into Azure Machine Learning Studio in minutes.As a developer who has never focused much on machine learning, I found this to be a great read for expanding my skills in the cloud into new areas. Azure has a leading set of tools, and this was coauthored by some of the creators and implementers responsible.With a better understanding of the process and experience with the steps involved, I can better support my teamâs Machine Learning Engineers in DevOps, data pipelines, and other related areas.
jay –
Only book that has everything you need for Azure Machine learning
Youâre one stop shop solution book for all of your AML needs – the authors have done a fantastic job to dig in and do a step-by-step process of using AML while going through the concepts, so will you understand everything that you need from Engineering perspective, machine learning and the steps that you need to take to execute your project.Super happy and highly recommend!!
P K. –
good book but horrible printing
I like this book. It covers basics of everything needed for a Azure ML Engineer and Data Scientist. But, the print is horrible. I do not understand why would the book printed black and white while the pdf is completely color.Black and white print makes very difficult to follow. Paying 40$ and getting a black and white book which original print is color is not worth it. I am going to return it and probably print the pdf in color format.Authors please consider printing in color print. It would looke really good.
H2N –
Good book in the market
The book covers topics about Azure Machine Learning from training and tuning models with Azure Service, Deploying and Expanding Models and especially, product ionizing workload with MLOps. It is structured into distinct sections, each dedicated to a specific aspect of the platform. The authors’ expertise, clear explanations, and practical code examples enhance the book’s value, and a GitHub repository with code samples accompanies it.
Prateek Gandhi –
Great book on operationalizing Azure ML and making it real !
I enjoyed reading this book as it provides a comprehensive and practical guide to Azure Machine Learning, a cloud-based service that empowers data scientists and developers to build, deploy, and manage high-quality models faster and with confidence. The book covers various topics such as data preparation, model training, model registration, model deployment, model monitoring, and MLOps. It also shows how to use Azure Machine Learning with open-source frameworks such as Pytorch, TensorFlow, or scikit-learn. The authors are experts in their fields and they explain the concepts clearly and concisely with code examples and screenshots. The book also includes a GitHub repository with all the code samples used in the book.One of the highlights of this book is that it also introduces Responsible AI principles and practices for developing and maintaining machine learning models ethically and reliably. It demonstrates how to use the Responsible AI dashboard integrated with Azure Machine Learning platform to assess model fairness, explainability, error analysis, causal analysis, performance, and data quality. It also provides tips on how to generate a Responsible AI scorecard report based on the dashboard insights.I would recommend this book to anyone who wants to learn how to use Azure Machine Learning effectively and efficiently for their machine learning projects. It is suitable for beginners as well as experienced practitioners who want to leverage the power of Azure Machine Learning service while adhering to Responsible AI standards.
Mickinch Patel –
Truly admirable work
Artificial Intelligence (AI) is taking all technologist to the new level. We can’t imagine AI without Machine Learning (ML). Most learning comes in theoretical way. This is first book that came very handy to learn and apply learning through practical steps. Flow of the books is just perfect.I am grateful to the authors taking learning and apply towards curating perfect examples, content and writeup.