Five Things on our Data and AI Radar for 2021

Here are some of the most significant themes we see as we look toward 2021. Some of these are emerging topics and others are developments on existing concepts, but all of them will inform our thinking in the coming year. MLOps attempts to bridge the gap between Machine Learning (ML) applications and the CI/CD pipelines that have become standard practice. ML presents a problem for CI/CD for several reasons. The data that powers ML applications is as important as code, making version control difficult; outputs are probabilistic rather than deterministic, making testing difficult; training a model is processor intensive and time consuming, making rapid build/deploy cycles difficult.