4 ways to successfully scale machine learning Domino Data Lab

Deploying machine learning models in a repeatable, scalable manner requires an understanding that the algorithms and techniques that underpin models are rapidly evolving and are managed differently than traditional software development tools. With significant advances happening in the open-source community, the tools, techniques and algorithms that your data scientists use today to solve business problems will undoubtedly change very soon. Of course, not all change is good, and in many situations, implementing techniques and approaches that aren't understood can lead to catastrophic failures. Here are some guiding principles to ensure your company increases its chance of being successful in its machine learning practices. Maslow's hammer talks of a cognitive bias that involves using a familiar tool: "If all I have is a hammer, every problem looks like a nail."