Survey: Machine Learning Working Best for Large Enterprises -- Pure AI

Survey: Machine Learning Working Best for Large Enterprises

By Richard Seeley
10/17/2018

Large enterprises are getting more bang for their bucks from machine learning (ML) initiatives and applications than smaller organizations, according to 523 data scientists and ML professionals surveyed this summer by Seattle-based Algorithmia Inc.

"State of Enterprise Machine Learning," the company's report released this week found -- perhaps not surprisingly -- that larger organizations, defined as having 2,500+ employees, are happier with their machine learning apps than smaller companies with 500 employees or less.

The ability of big businesses to hire data science talent and bankroll major projects appears to make a big difference in the successful implementation of ML.

"In 2018, large enterprise companies have an advantage when it comes to machine learning because they have access to more data, can continue to invest in big R&D efforts, and have many problems that machine learning technology can solve cost-effectively," Diego Oppenheimer, CEO at Algorithmia, was quoted as saying in today's press release. "And yet, even in the largest companies, productionizing and managing machine learning models remains a challenge."

Big tech-based companies including Google, Facebook and Uber are creating a new ML-based infrastructure, which Algorithmia dubs the "AI Layer." An AI Layer manages compute loads, automates deployment of ML models, and propagates ML throughout the company, according to Algorithmia.

But as Oppenheimer noted the ML adoption and application is not without challenges even for tech giants.

The Algorithmia survey, which focused on organizations in North America, found that while ML is getting a big boost from "massive investments of time, money and focus," human intelligence appears to be dogging artificial intelligence efforts. "For example, data science and machine learning teams are spending too much time on infrastructure, deployment and engineering, and not nearly enough (less than 25 percent) on training and iterating models," the report on the survey results concludes.

To show where the survey respondents see ML roadblocks, Algorithmia cited the following results:

38 percent of respondents reported difficulty in deploying models to the needed scale. Anecdotally, the reasons include: DevOps and IT teams not having sufficient resources; data scientists being expected to build the infrastructure to put their models into production; and a lack of existing infrastructure within the organization to support the needs of running ML models at scale.
30 percent of respondents reported challenges in supporting different programming languages and training frameworks. Machine learning models often are created using a number of different programming languages and training frameworks. This adds an additional level of complexity because several models, written in distinct languages and frameworks, must be pipelined together for a given task. Larger companies, like Facebook and Uber, have solved some of these issues by building large internal tools such as FBLearner and Michelangelo, respectively.
30 percent reported challenges in model management tasks such as versioning and reproducibility.

Despite these challenges Oppenheimer sees a bright future for ML in large and small enterprises: "In general, larger companies have more machine learning use-cases in production than smaller companies. But across the board, all companies are getting smarter about where and how to apply ML technology. We expect to see big leaps in productionized machine learning next year as data scientists can more easily deploy and manage their models."