Recommendations – running AL/ML workloads on Kubernetes
If you’re a data scientist or ML engineer, you’re probably thinking about how to deploy your ML models efficiently. You would essentially look for ways to scale models, distribute them across server clusters, and optimize model performance with a variety of techniques.
These are all tasks that Kubernetes is very good at. But Kubernetes was not designed to be an ML deployment platform. However, as more data scientists turn to Kubernetes to run their models, Kubernetes and ML are becoming popular stacks.
As a platform for training and deploying ML models, Kubernetes provides several key advantages. To understand those benefits, let’s compare some of the major challenges and Kubernetes solution offerings:
Table 9.2 – Kubernetes solution offerings
Kubernetes helps offset some of the most significant challenges that data scientists face when running...
