7 lessons to ensure successful machine learning projects

Every organization has machine learning opportunities, but finding the right team and the right uses can be a challenge.

When Michelle K. Lee, ’88, SM ’89, was sworn in as the director of the U.S. Patent and Trademark Agency in 2015, she saw an opportunity. The agency was a bit behind on digital transformation and adopting things like cloud computing and artificial intelligence, but the organization had mountains of data — like more than 10 million patents the office has issued since opening in 1802, and 600,000 patent applications received each year.

Lee led a project to use data and analytics to modernize the agency, such as implementing AI solutions to improve patent searches and the speed and quality of patents issued. By gathering data about how patent examiners make decisions, and determining outlying behavior, the office could also pinpoint areas in which examiners would benefit from targeted training.

“If the U.S. Patent and Trademark Office, a 200-plus-year-old governmental agency, has a machine learning opportunity, so too does every organization,” Lee said during a presentation at EmTech Digital, hosted by MIT Technology Review. “The challenge is in identifying those opportunities, and having a team and plan to implement them.”

Lee, who is now the vice president of machine learning at Amazon Web Services and a full-term member of the MIT Corporation, said she’s seen businesses in a wide range of industries successfully using machine learning. She’s also seen some common stumbling blocks, like businesses struggling to find the best use cases for machine learning, businesses failing to have easy access to their data, and businesses lacking necessary technical talent and expertise.

Here are her insights on how to ensure successful machine learning projects:

Successful machine learning solutions start with a strong data strategy. “Your machine learning model is only as good as the data it’s trained on, and data is often cited as the number one challenge to adopting machine learning,” Lee said.

If there are problems with the data, machine learning scientists will end up spending their time doing data cleanup and management, or they’ll get frustrated because they don’t have the data they need, she said.

Companies should make sure they have the three hallmarks of a strong data strategy:

In addition, Lee suggested four questions to ask when beginning machine learning projects:

Businesses should start by defining their business problems, seeing which ones could be solved with machine learning, and outlining clear metrics to measure success, Lee said. Things to keep in mind include data readiness, business impact, and machine learning applicability. A high-impact business use case, without much data or machine learning applicability, will result in frustrated data scientists. A use case with lots of data and high machine learning applicability but low business impact probably won’t be adopted. Success metrics could include impact on revenue and efficiency.

“Every company has a machine learning opportunity,” Lee said.