Subscribe / Unsubscribe Enewsletters | Login | Register

Pencil Banner

10 tips for getting started with machine learning

Clint Boulton | Sept. 13, 2017
Artificial intelligence and machine learning can yield game-changing solutions for enterprises. Here’s what senior IT leaders need to know to launch and maintain a successful machine learning strategy.

 

1. Understand where data science fits

You have an idea for leveraging data science and ML at your organization, but how do you go about implementing it? First, you needn’t centralize your data science and ML operations. In fact, it may make sense to embed data science and machine learning into every department, including sales, marketing, HR and finance. Olley suggested CIOs try something that works for him at Elsevier, where he pairs data scientists with software engineers or oncology specialists, who build products in agile squads inspired by the Spotify model.

Machine learning use cases 
Dan Olley's machine learning use cases presentation slide from CIO100 Symposium. Credit: Elsevier

“We've built our data science teams into our product management teams and business units but we bring them together as a chapter and have one person lead that,” Olley said. “We do put the data scientists as close to the problem as we possibly can because we think that's the way to scale across the organization better.”

 

2. Get started

You needn't have a five-point plan for building a data science enterprise nor a framework to construct a polished ML product. Gartner says you should foster small experiments in different business areas with particular AI technologies for learning purposes, not ROI. "If you haven't yet I thoroughly recommend that you get started," Olley said. "Your competitors are."

 

3. Treat your data as if it's money

With data serving as the fuel for any AI/ML efforts, CIOs must treat their data like it's money by managing it, protecting it and obsessing over it. "Your CFO wouldn't just let the accounts be spread all over the company," Olley said. "Nor would he or she say, 'I think we've got about this much in revenue this year.'"

 

4. Stop looking for purple squirrels

Data scientists tend to be people who have high aptitude in math and statistics and are skilled at finding insights in data, not necessarily software engineers that can write algorithms and craft products. This is easier said than done as companies often seek unicorn-like candidates who are master statisticians, ninja software engineers and masters in an industry domain, such as health care or financial services, Olley said. "I heard one person describe it as, 'I want a software engineer with a Ph.D. in mathematics who is also a trained clinician and if they have a specialty in oncology that would be really useful,'" Olley said, wryly adding that he knows "those three people."

 

5. Build a data science training curriculum

Not everyone who practices data science is going to be a data scientist or require a black belt in the craft. "You're not going to find enough of these people so you're going to have work out how to train them,” Olley said, noting that he has a person responsible for “upskilling” his IT staff in data science. Elsevier has also leveraged Coursera for help. Olley at least recommends that CIOs create a refresher course in probability and statistics, with a final exam candidates must pass to prove their mettle. Gartner advises you to identify AI knowledge and talent gaps and develop a training and hiring plan to build out your capabilities.

 

Previous Page  1  2  3  Next Page 

Sign up for CIO Asia eNewsletters.