At a rapid pace, many machine learning techniques have moved from proof of concepts to powering crucial pieces of technology that people rely on daily. In attempts to capture this newly unlocked value, many teams have found themselves caught up in the fervor of productionizing machine learning in their product without the right tools to do so successfully. The truth is, we are in the early innings of defining what the right tooling suite will look like for building, deploying, and iterating on machine learning models. In this piece we will talk about the only 3 ML tools you need to make your team successful in applying machine learning in your product. Before we jump into our ML stack recommendations, let's turn our attention quickly to how the tooling that the software engineering industry has settled on.

I have spent most of my professional life in the age of AI and ML. During earlier times at Uber, I worked with models that estimated ETAs, calculated dynamic pricing and even matched riders with drivers. My co-founder Jason previously led video ad company TubeMogul (acquired by Adobe), which relied on ML to ensure that its advertisers didn't waste their media spend on ads that nobody saw, or ads that only bots saw. Although ride-sharing and video advertising aren't often used in the same sentence, both Jason and I faced similar challenges in ensuring that the models our companies deployed worked effectively and without bias. When models don't work as planned and machines, trained by data, make bad decisions, there is a direct impact on business results.