Big businesses take the lead in machine learning

Data science and machine learning professionals within larger organizations are feeling significantly more satisfied with their progress than those in smaller organizations, according to a new study.

The report from machine learning specialist Algorithmia shows that those in organizations with 2,500+ employees are 80 percent more likely to be 'satisfied' or 'very satisfied' with their progress as compared to professionals in companies of 500 employees or less.

Among other key findings are that 92 percent of respondents in companies with 10,000 employees or more say their organization's investment in machine learning has grown by at least 25 percent in the past year. In companies with fewer than 10,000 employees By comparison, 80 percent of respondents say their organization's investment has grown by at least 25 percent in the past 12 months.

In the largest enterprises, the biggest machine learning use-case is increasing customer loyalty (59 percent), followed by increasing customer satisfaction (51 percent) and interacting with customers (48 percent). Larger organizations more heavily emphasize the use of data science to save money, too. 48 percent of respondents in companies with 10,000+ employees cite cost savings as a primary ML use-case, compared to 43 percent in companies of 1,001-2,500 employees and 41 percent in companies with 2,501-10,000 employees.

Large tech companies have created a new category of infrastructure -- which Algorithmia refers to as the 'AI Layer' -- to manage compute loads, automate the deployment of machine learning models, and provide tools for managing machine learning across the organization. Some examples of AI Layers created by big tech companies include FBLearner from Facebook, TFX from Google and Michelangelo from Uber.

"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."

When it comes to deploying machine learning, teams are spending too much time on infrastructure, deployment and engineering, and not enough (less than 25 percent) on training and repeating models.

Other challenges include difficulty deploying models to the necessary scale (38 percent), problems supporting different programming languages and training frameworks (30 percent), with a further 30 percent reporting challenges in model management tasks such as versioning and reproducibility.

"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 next year as data scientists can more easily deploy and manage their models."

You can download the full State of Machine Learning whitepaper from the Algorithmia site.

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