Data scientists are painfully familiar with the frustration of a great machine learning model that was doomed to be stuck in the POC stage. It’s no secret that while most organizations understand the importance of machine learning, most initiatives never make it off the ground, or produce the impact they were designed to provide. How can you avoid this fate, and push your models all the way to deployment? This guide breaks down four steps to build models that will be scalable and adaptable and go from theory to deployment faster.
