Machine learning has become an important tool in the modern software toolbox, and high-performing organizations are increasingly coming to rely on data science and machine learning as a core part of their business. eBay introduced machine learning to its commerce search ranking and drove double-digit increases in revenue. Stitch Fix built a multibillion dollar clothing retail business in the US by combining the best of machines with the best of humans. And WeWork is bringing machine-learned approaches to the physical office environment all around the world. In all cases, algorithmic techniques started simple and slowly became more sophisticated over time. This talk will use these examples to derive an agile approach to machine learning, and will explore that approach across several different dimensions. We will set the stage by outlining the kinds of problems that are most amenable to machine-learned approaches as well as describing some important prerequisites, including investments in data quality, a robust data pipeline, and experimental discipline. Next, we will choose the right (algorithmic) tool for the right job, and suggest how to incrementally evolve the algorithmic approaches we bring to bear. Most fancy cutting-edge recommender systems in the real world, for example, started out with simple rules-based techniques or basic regression. Finally, we will integrate machine learning into the broader product development process, and see how it can help us to accelerate business results