Data science and machine learning are on the rise at insights-driven enterprises. However, surviving and thriving means not only having the right platforms, tools and skills, but identifying use cases and implementing processes that can deliver repeatable, scalable business value. The challenges are numerous, from infrastructure management, to data preparation and exploration, model training and deployment. In response, new solutions have emerged, along with the rise of DataOps, to address key needs in areas including self-service, real-time and visualization.