By most estimates, fewer than 20% of machine learning projects make it past the initial pilot stage. In order to succeed, data scientists must work closely with developers and IT operations teams to plan for future needs and model success to help justify costs. This webcast will explore the biggest challenges to implementing a machine learning project, where teams get tripped up and how to avoid these roadblocks. It will also cover advice for IT leaders about how to scale and support a machine learning project as it grows.