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 or 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.