Companies of all sizes are not satisfied with their machine learning process and various challenges to widespread adoption remain.
SEATTLE, Oct. 16, 2018 (GLOBE NEWSWIRE) -- Algorithmia announces the results of a survey on enterprise machine learning. The comprehensive survey, titled “State of Enterprise Machine Learning,” is a first for Algorithmia and was designed to explore the ways in which companies of all sizes are utilizing machine learning. The survey was completed by over 500 data science and machine learning professionals, the majority of whom were based in North America. A report detailing the survey’s findings can be found here.
A key takeaway from the survey was that data science and machine learning professionals within larger organizations (2,500+ employees) are feeling significantly more satisfied with their progress than those in smaller organizations—they are roughly 300% more likely to consider their model deployment “sophisticated” and 80% more likely to be “satisfied” or “very satisfied” with their progress as compared to professionals in companies of 500 employees or less.
Following are other key findings from the survey related to machine learning in the enterprise:
“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,” says Diego Oppenheimer, CEO at Algorithmia. “And yet, even in the largest companies, productionizing and managing machine learning models remains a challenge. Productionizing models is seen as the last step to ROI. Without an enterprise platform to help, these companies are missing out on the rewards of machine learning.”
Resource and Infrastructure Issues
Despite the fact that machine learning is benefiting from massive investments of time, money and focus, a variety of production challenges remain. 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%) on training and iterating models. Other challenges include:
“In general, larger companies have more machine learning use-cases in production than smaller companies,” says Oppenheimer. “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 over 2019 as data scientists can more easily deploy and manage their models.”
Algorithmia is the leader in Machine Learning deployment. The Algorithmia AI Layer deploys and manages models in pioneering Fortune 100 companies, as well as US Intelligence Agencies and the United Nations. Algorithmia.com has nearly 80,000 engineers and Data Scientists deploying models on the AI Layer. For more information, visit www.algorithmia.com.