Code: USD75OFF
This is the code repository for Azure Machine Learning Engineering, published by Packt.
Deploy, fine-tune, and optimize ML models using Microsoft Azure
Data scientists working on productionizing machine learning 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 Azure Machine Learning Service. You’ll see how data scientists and machine learning engineers working with Microsoft Azure can train and deploy ML models at scale by putting their knowledge to work with this practical guide.
This book covers the following exciting features:
- Train machine learning models in Azure Machine Learning Service
- Build end-to-end machine learning pipelines
- Host machine learning models on real-time scoring endpoints
- Mitigate bias in machine learning models
- Get the hang of using an MLOps framework to productionize models
- Simplify machine learning model explainability using AMLS and Azure Interpret
If you feel this book is for you, get your copy today!
All of the code is organized into folders.
The command will look like the following:
az extension remove -n azure-cli-ml
az extension remove -n ml
Following is what you need for this book: 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.
With the following software and hardware list you can run all code files present in the book.
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
Sina Fakhraee, Ph.D., is currently working at Microsoft as an enterprise data scientist and senior cloud solution architect. He has helped customers to successfully migrate to Azure by providing best practices around data and AI architectural design and by helping them implement AI/ML solutions on Azure. Prior to working at Microsoft, Sina worked at Ford Motor Company as a product owner for Ford’s AI/ML platform. Sina holds a Ph.D. degree in computer science and engineering from Wayne State University and prior to joining the industry, he taught various undergrad and grad computer science courses part time. If you would like to know more about Sina, please visit his LinkedIn: https://www.linkedin.com/in/sina-fakhraee-ph-d-2798ba70/. I would like to thank my manager, Rod Means, for his outstanding guidance, support, and leadership over the past few years. I would also like to thank Ali Abidi, Priyanka Soam, Kirti Pisat, and the rest of the team at Packt for their help and support throughout the process. I would like to thank my amazing team members, Bala and Megan, for their amazing collaboration and teamwork.
Balamurugan Balakreshnan is a principal cloud solution architect at Microsoft Data/AI Architect and Data Science. He has provided leadership on digital transformations with AI and cloud-based digital solutions. He has also provided leadership in terms of ML, the IoT, big data, and advanced analytical solutions. A big thank you to my manager Shruti Harish for her guidance and support throughout the book. I also thank the publishers, Packt, and their team – Ali Abidi, Priyanka Soam, Kirti Pisat, and rest of the team. Thank you to all my friends and colleagues for providing me with this wonderful opportunity to collaborate on the book (Sina Fakhraee and Megan Masanz).
Megan Masanz is a principal cloud solution architect at Microsoft focused on data, AI, and data science, passionately enabling organizations to address business challenges through the establishment of strategies and road maps for the planning, design, and deployment of Azure Cloud-based solutions. Megan is adept at paving the path to data science via computer science given her master’s in computer science with a focus on data science (https://meganmasanz.azurewebsites.net/). I would like to thank my manager, Marc Grove, a wonderful source of support and guidance. I would like to thank the Packt team for their partnership in bringing this book forward, and for the opportunity they have provided. I would like to thank my team members for their amazing collaboration and teamwork
If you have already purchased a print or Kindle version of this book, you can get a DRM-free PDF version at no cost.
Simply click on the link to claim your free PDF.