Build vs buy has long been a key question for those delivering platforms and tooling to support the machine learning lifecycle. Just a few years ago, the most sophisticated ML teams all built their own tooling because there was little available to buy. Today however we are on the other side of an explosion in high quality open source and commercial ML tooling, which more to come. So what’s the best approach to take for modern enterprise ML teams? How do you map the space of options to the unique needs of your organization? And what should you definitely be building or definitely be buying, if you need it today?
We’ll answer these questions and many more in our Build vs Buy in 2022 panel.
