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 (AutoML), 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 ML models may be limited in how much they can explore. Once you complete this book, you’ll understand how to apply AutoML to your data right away.
- Learn how companies in different industries are benefiting from AutoML
- Get started with AutoML using Azure
- Explore aspects such as algorithm selection, auto featurization, and hyperparameter tuning
- Understand how data analysts, BI professions, developers can use AutoML in their familiar tools and experiences
- Learn how to get started using AutoML for use cases including classification, regression, and forecasting.
From the Forward
I vividly remember my first undergraduate class in artificial intelligence (AI). My father had worked for years on “expert systems,” and I was at MIT to learn from the best how to perform this wizardry. Marvin Minsky, one of the founders of the field, even taught a series of guest lectures there. It was about midway through the semester when the great disillusionment hit me: “It’s all just a bunch of tricks!” There was no “intelligence” to be found; just a bunch of brittle rules engines and clever use of math. This was in the early ’90s and the start of my own personal AI winter, when I dismissed AI as not having much use.
Years later, while I was working on advertising systems, I finally saw that there was power in this “bunch of tricks.” Algorithms that had been hand-tuned for months by talented engineers were being beaten by simple models provided with lots of data. I saw that the explosion that was to come simply needed more data and more computation to be effective.
Over the past 5 to 10 years, the explosion in both big data and computation power has unleashed an industry that has had lots of starts and stops to it.
This time is different. While the hype about AI is still tremendously high, the potential applications of practical AI have really just begun to hit the business world.
The rules/people making predictions today will be replaced virtually every place by AI algorithms. The value AI creates for businesses is tremendous, from being better able to value the oil available in an oil field to better predicting the inventory a store should stock of each new sneaker. Even marginal improvements in these capabilities represent billions of dollars of value across businesses.
We’re now in an age of AI implementation. Companies are working to find all the best places to deploy AI in their enterprises. One of the biggest challenges is matching the hype to reality. Half the companies I’ve talked to expect AI to perform some kind of magic for problems they have no idea how to solve. The other half are underestimating the power that AI can have. What they need are people with enough background in AI to help them conceive of what is possible and apply it to their business problems.
Customers I talk to are struggling to find enough people with those skills. While they have lots of developers and data analysts who are skilled and comfortable making predictions and decisions with data, they need data scientists who can then build the model from that data. This book will help fill that gap.
It shows how automated ML can empower developers and data analysts to train AI models. It highlights a number of business cases where AI is a great fit to the business problem and show exactly how to build that model and put it into production.
The technology and ideas in this book have been pressure-tested at scale with teams all across Microsoft, including Bing, Office, Azure Security, internal IT, and many more. It’s also been used by many external businesses using Azure Machine Learning.
_Eric Boyd,Microsoft Corporate Vice President, Azure AI.September 2019
Part I. Automated Machine Learning
Chapter 1. Machine Learning: Overview and Best Practices
Chapter 2. How Automated Machine Learning Works
Part II. Automated ML on Azure
Chapter 3. Getting Started with Microsoft Azure Machine Learning and Automated ML
Chapter 4. Feature Engineering and Automated Machine Learning
Chapter 5. Deploying Automated Machine Learning Models
Chapter 6. Classification and Regression
Part III. How Enterprises Are Using Automated Machine Learning
Chapter 7. Model Interpretability and Transparency with Automated ML
Chapter 8. Automated ML for Developers
Chapter 9. Automated ML for Everyone
- Title:Practical Automated Machine Learning on Azure: Using Azure Machine Learning to Quickly Build AI Solutions
- Length: 223 pages
- Edition: 1st
- Language: English
- Publisher: O’Reilly Media;
- Publication Date: September 23, 2019
- ISBN-10: 149205559X
- ISBN-13: 978-1492055594