Understanding how OpenShift supports MLOps
As you have seen, an application platform provides an opinionated way of running services on Kubernetes. An example of this is OpenShift, which provides Prometheus and Grafana as monitoring services. A similar approach is applied to the software required to run MLOps on OpenShift. Red Hat and its partners provide MLOps components on top of the OpenShift platform that provides the services for a complete ML platform. Using OpenShift, all the MLOps capabilities can be consistently deployed on-premises and on the cloud.
Just like DevOps, one of the primary objectives of MLOps is to bridge the gap between the engineers who are building the applications – or in this case, the data scientists and ML engineers who are developing ML models – and the operations team. To achieve this, we need to have a common platform where engineers and operators will meet. The best tool we have for this is containerization platforms. This allows both...
