Creating and deploying machine learning (ML) models supposedly takes too much time. Quantifying this problem is difficult, not least because there are so many job roles involved with a machine learning pipeline. With that caveat, let us introduce Algorithmia’s “2020 State of Enterprise ML.” Conducted in October 2019, 63% of the 745 respondents have already developed and deployed a machine learning model into production. On average, 40% of companies said it takes more than a month to deploy an ML model into production, 28% do so in eight to 30 days, while only 14% could do so in seven days or less.
We believe Algorithmia’s estimate is much closer to reality than that reported in a Dotscience survey from earlier in the year that reported 80% of respondents’ companies take more than six months to deploy an artificial intelligence (AI) or ML model into production. That data point is misleading because it includes respondents that are still evaluating use cases and are in the process of deploying their first ML model. Of course, this in and of itself a substantial concern. In fact, 78% of AI or ML projects involving training an AI model stall at some point before deployment according to another 2019 survey, this one of 277 data scientists and AI professionals by data labeling company Alegion.
The complete article can be found here.
