 Companies are deploying more and more AIML and data analytics into their intelligent applications. But how many are really truly getting those into production at scale? It starts with a connected team. Companies must bring together developers, IT operations, data scientists, and ML engineers to best operationalize ML ops. Here are some things to consider when taking this on. Build a data strategy. Answer questions like, how will this data be gathered and stored? How will it be used to provide insights? Develop a plan to handle cleansing, storing, securing, preparing, and monitoring this data to prevent inaccurate predictions. Provide data scientists and developers easy self-service access to tools. You can't impose overly restrictive access to data science tools or have users wait forever for a help ticket to get answered and expect success. Create a collaborative environment. ML ops integrates data scientists into the DevOps CICD workflow for the entire AIML life cycle. Use a common hybrid cloud application development platform based on containers, integrated DevOps capabilities, and a technology ecosystem to speed up your workflow. A hybrid cloud approach lets you move from the edge to the data center to the public cloud as workloads and data locality demand. Choosing a hybrid cloud platform powered by Kubernetes and DevOps capabilities provides consistency across all these footprints as you develop, test, and manage applications. And finally, choose open. Open source keeps users from being locked into restrictions imposed by a single cloud provider and gives everyone access to a wide breadth of technologies. Here at Red Hat, we keep these considerations in mind. We've developed Red Hat OpenShift Data Science. We hope to give data scientists and developers a powerful AI platform to do what they do best, build intelligent applications.