Key considerations for building an AI platform

Check out the machine learning sessions at the Strata Data Conference in London, May 21-24, 2018. The promises of AI are great, but taking the steps to build and implement AI within an organization is challenging. As companies learn to build intelligent products in real production environments, engineering teams face the complexity of the machine learning development process--from data sourcing and cleaning to feature engineering, modeling, training, deployment, and production infrastructure. Core to addressing these challenges is building an effective AI platform strategy--just as Facebook did with FBLearner Flow and Uber did with Michelangelo. Often, this task is easier said than done.