Data science and machine learning professionals within large organisations are feeling significantly more satisfied with their machine learning progress than those in smaller organisations, according to a new survey.
The survey, titled “State of Enterprise Machine Learning”, was conducted by Algorithmia, a specialist in machine learning. 500 data science and machine learning professionals were asked questions about the state of their organisation’s machine learning deployments.
The survey reveals that professionals within larger organisations are 300% more likely to consider their model deployment “sophisticated” and 80% more likely to be satisfied with their progress compared to professionals in companies with 500 employees or less.
Big business investment in machine learning is also growing at a faster rate compared to investment by small and medium businesses. 92% of respondents in companies with 10,000 employees or more said their organisation’s investment in machine learning had grown by at least 25% in the past year.
In comparison, 80% of respondents in companies with fewer than 10,000 employees said their organisation’s investment grew at least 25% in the past 12 months.
According to Algorithmia, large tech companies have an advantage over their competition as they have created a new category of infrastructure called the “AI Layer” that helps manage compute loads, automate machine learning model deployment, and streamline machine learning management.
Examples of AI Layers 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,” said Diego Oppenheimer, CEO at Algorithmia.
However, Oppenheimer cautioned that even large companies are struggling to ‘productionize’ and manage their machine learning models.
“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,” he said.
In addition to management challenges, 38% of respondents reported difficulty in deploying models at scale. Algorithmia says the difficulty is down to a lack of resources and infrastructure available to DevOps and IT teams.
“In general, larger companies have more machine learning use-cases in production than smaller companies,” added 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.”