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Practical Automated Machine Learning on Azure

Using AutoML to Build and Deploy Intelligent Solutions

Paperback Engels 2019
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Develop smart applications without spending days and weeks building machine-learning models. With this practical book, you’ll learn how to apply Automated Machine Learning, a process that uses machine learning to help people build machine learning models. Deepak Mukunthu, Parashar Shah, and Wee Hyong Tok provide a mix of technical depth, hands-on examples, and case studies that show how customers are solving real-world problems with this technology.

Building machine learning models is an iterative and time-consuming process. Even those who know how to create these models may be limited in how much they can explore. Once you complete this book, you’ll understand how to apply Automated Machine Learning to your data right away.

- Learn how companies in different industries are benefiting from Automated Machine Learning
- Get started with Automated Machine Learning using Azure
- Explore aspects such as algorithm selection, auto featurization, and hyperparameter tuning
- Understand how data analysts, BI professionals, and developers can use Automated Machine Learning in their familiar tools and experiences
- Learn how to get started using Automated Machine Learning for use cases including classification and regression.


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Over Deepak Mukunthu

Deepak Mukunthu is a product leader with more than 16 years of experience. With his experience in big data, analytics, and AI, Deepak has played instrumental leadership roles in helping organizations and teams become data-driven and to adopt machine learning. He brings a good mix of thought leadership, customer understanding, and innovation to design and deliver compelling products that resonate well with customers. In his current role of principal program manager of the automated ML in Azure AI platform group at Microsoft, Deepak drives product strategy and roadmap for Automated ML with the goal of accelerating AI for data scientists and democratizing AI for other personas interested in machine learning. In addition to shaping the product direction, he also plays an instrumental role in helping customers adopt Automated ML for their business-critical scenarios. Prior to joining Microsoft, Deepak worked at Trilogy where he played multiple roles—consultant, business development, program manager, engineering manager—successfully leading distributed teams across the globe and managing technical integration of acquisitions.

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Over Parashar Shah

Parashar Shah is a senior program/product manager on the Azure AI engineering team at Microsoft, leading big data and deep learning projects to help increase adoption of AI in enterprises especially automated ML with Spark. At Microsoft and at Alcatel-Lucent/Bell Labs prior to that, his contributions increased global adoption of AI/analytics platform contributing to customers' growth in retail, manufacturing, telco, and oil and gas verticals. Parashar has an MBA from the Indian Institute of Management Bangalore and a B.E. (E.C.) from Nirma Institute of Technology, Ahmedabad. He also cofounded a carpool startup in India. He has also coauthored Hands-On Machine Learning with Azure: Build Powerful Models with Cognitive Machine Learning and Artificial Intelligence (Packt), published in November 2018. He has filed for five patents. He has presented at multiple Microsoft and external conferences, including Spark summit and KDD. His interests span the subjects of photography, AI, machine learning, automated ML, big data, and the internet of things (IoT).

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Over Wee-Hyong Tok

Wee Hyong Tok is part of the AzureCAT team at Microsoft. He has extensive leadership experience leading multidisciplinary team of engineers and data scientists, working on cutting-edge AI capabilities that are infused into products and services. He is a tech visionary with a background in product management, machine learning/deep learning and working on complex engagements with customers. Over the years, he has demonstrated that his early thought leadership whitepapers on tech trends have become reality, and deeply integrated into many products. His ability to strategize, and turn strategy to execution, and hunting for customer adoption has enabled many projects that he works on to be successful. He is continuously pushing the boundaries of products for machine learning and deep learning. His team works extensively with deep learning frameworks, ranging from TensorFlow, CNTK, Keras, and PyTorch. Wee Hyong has worn many hats in his career—developer, program/product manager, data scientist, researcher, and strategist—and his range of experience has given him unique superpowers to lead and define the strategy for high-performing data and AI innovation teams. Throughout his career, he has been a trusted advisor to the C-suite, from Fortune 500 companies to startups.

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Conventions Used in This Book
Using Code Examples
O’Reilly Online Learning
How to Contact Us

I: Automated Machine Learning
1. Machine Learning: Overview and Best Practices
Machine Learning: A Quick Refresher
Model Parameters
Best Practices for Machine Learning Projects
Understand the Decision Process
Establish Performance Metrics
Focus on Transparency to Gain Trust
Embrace Experimentation
Don’t Operate in a Silo
An Iterative and Time-Consuming Process
Feature Engineering
Algorithm Selection
Hyperparameter Tuning
The End-to-End Process
Growing Demand

2. How Automated Machine Learning Works
What Is Automated Machine Learning?
Understanding Data
Detecting Tasks
Choosing Evaluation Metrics
Feature Engineering
Selecting a Model
Monitoring and Retraining
Bringing It All Together
Automated ML
How Automated ML Works
Preserving Privacy
Enabling Transparency
End-to-End Model Life-Cycle Management

II: Automated ML on Azure
3. Getting Started with Microsoft Azure Machine Learning and Automated ML
The Machine Learning Process
Collaboration and Monitoring
Setting Up an Azure Machine Learning Workspace for Automated ML
Azure Notebooks
Notebook VM

4. Feature Engineering and Automated Machine Learning
Data Preprocessing Methods Available in Automated ML
Auto-Featurization for Automated ML
Auto-Featurization for Classification and Regression
Auto-Featurization for Time-Series Forecasting

5. Deploying Automated Machine Learning Models
Deploying Models
Registering the Model
Creating the Container Image
Deploying the Model for Testing
Testing a Deployed Model
Deploying to AKS
Swagger Documentation for the Web Service
Debugging a Deployment
Web Service Deployment Fails

6. Classification and Regression
What Is Classification and Regression?
Classification and Regression Algorithms
Using Automated ML for Classification and Regression
III. How Enterprises Are Using Automated Machine Learning

7. Model Interpretability and Transparency with Automated ML
Model Interpretability
Model Interpretability with Azure Machine Learning
Model Transparency
Understanding the Automated ML Model Pipelines

8. Automated ML for Developers
Azure Databricks and Apache Spark
SQL Server

9. Automated ML for Everyone
Azure Portal UI
Power BI
Preparing the Data
Automated ML Training
Understanding the Best Model
Understanding the Automated ML Training Process
Model Deployment and Inferencing
Enabling Collaboration
Azure Machine Learning to Power BI
Power BI Automated ML to Azure Machine Learning


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