Managing risk in machine learning

[A version of this post appears on the O’Reilly Radar.]

Considerations for a world where ML models are becoming mission critical.

In this post, I share slides and notes from a keynote I gave at the Strata Data Conference in New York last September. As the data community begins to deploy more machine learning (ML) models, I wanted to review some important considerations.

Let’s begin by looking at the state of adoption. We recently conducted a surveywhich garnered more than 11,000 respondents—our main goal was to ascertain how enterprises were using machine learning. One of the things we learned was that many companies are still in the early stages of deploying machine learning (ML):

As far as reasons for companies holding back, we found from a survey we conducted earlier this year that companies cited lack of skilled people, a “skills gap,” as the main challenge holding back adoption.

Interest on the part of companies means the demand side for “machine learning talent” is healthy. Developers have taken notice and are beginning to learn about ML. In our own online training platform (which has more than 2.1 million users), we’re finding strong interest in machine learning topics. Below are the top search topics on our training platform:
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Lessons learned while helping enterprises adopt machine learning

[A version of this post appears on the O’Reilly Radar blog.]

The O’Reilly Data Show Podcast: Francesca Lazzeri and Jaya Mathew on digital transformation, culture and organization, and the team data science process.

In this episode of the Data Show, I spoke with Francesca Lazzeri, an AI and machine learning scientist at Microsoft, and her colleague Jaya Mathew, a senior data scientist at Microsoft. We conducted a couple of surveys this year—“How Companies Are Putting AI to Work Through Deep Learning” and “The State of Machine Learning Adoption in the Enterprise” — and we found that while many companies are still in the early stages of machine learning adoption, there’s considerable interest in moving forward with projects in the near future. Lazzeri and Mathew spend a considerable amount of time interacting with companies that are beginning to use machine learning and have experiences that span many different industries and applications. I wanted to learn some of the processes and tools they use when they assist companies in beginning their machine learning journeys.

Here are some highlights from our conversation:

Team data science process

Francesca Lazzeri: The Data Science Process is a framework that we try to apply in our projects. Everything begins with a business problem, so external customers come to us with a business problem or a process they want to optimize. We work with them to translate these into realistic questions, into what we call data science questions. And then we move to the data portion: what are the different relevant data sources, is the data internal or external? After that, you try to define the data pipeline. We start with the core part of the data science process—that is, data cleaning—and proceed to feature engineering, model building, and model deployment and management.
Continue reading “Lessons learned while helping enterprises adopt machine learning”