• by Gavin O'Malley  @mp_gavin, December 17, 2019

Although a growing number of companies are investing in machine learning, challenges in deployment, scaling and versioning, it is difficult to extract value from these investments.

That’s the big takeaway from Algorithmia’s latest survey, which included responses from nearly 750 business leaders. Perhaps the greatest investment that companies are making is in sheer manpower.

Indeed, half of professionals polled said their companies now employ between one and 10 data scientists.

That is actually down from 2018, when 58% of respondents said their companies employed between one and 10 data scientists, but drilling into the data paints a more complicated picture.

In 2018, 18% of companies said they employed 11 or more data scientists. This year, however, that number climbed to 39%, which suggests to Algorithmia that organizations are ramping up their hiring efforts to build larger data science arsenals, with some starting with to 10 data scientists.

Last year, just 2% of companies had more than 1,000 data scientists; today that share is just over 3%.

Not surprisingly, these companies include tech giants like Facebook, Apple, Amazon, Netflix, and Google. The research also found that half of companies spend between eight to 90 days deploying a single machine-learning model.

Asked why they didn’t deploy more models, 33% of respondents cited scaling issues, while 32% blamed reproducibility, and 26% pointed to overly cautious executives.

For its latest survey, Algorithmia also asked respondents what their companies are doing with their machine-learning technology.

The most common response was “reducing costs” (38%), followed by “generating customer insights and intelligence” (37%), and “improving the customer experience” (34%).

Other popular responses included using machine learning for “internal processing automation” (30%), “retaining customers” (29%), and “interacting with customers” (28%).