G2

Saurabh R.

Solution Architect

Enterprise (> 1000 emp.)

9/18/2026

"Solid Enterprise ML Platform, Steep Learning Curve"

4.5/5

What do you like best about Red Hat OpenShift Data Science?

What I like best is that it runs the entire ML workflow on OpenShift itself. Jupyter notebooks come up quickly with ready-made images for TensorFlow and PyTorch, and model serving happens on the same cluster, so moving from experiment to deployment is smooth. It's built on open-source tools, which avoids lock-in, and enterprise security and access control come for free. Review collected by and hosted on G2.com.

What do you dislike about Red Hat OpenShift Data Science?

The learning curve is steep if you're not already familiar with OpenShift or Kubernetes, and initial setup and cluster configuration can take real effort. It's also resource-heavy and can get expensive for smaller teams. The documentation sometimes lags behind new features, so troubleshooting pipelines or model serving issues can take longer than expected. Review collected by and hosted on G2.com.

What problems is Red Hat OpenShift Data Science solving and how is that benefiting you?

It solves the gap between data science and operations. Before, models were built on local machines and handing them off for deployment was slow and inconsistent. Now we develop, train, and serve models on one shared platform, which gives us consistent environments, faster deployment, and better governance. It's cut setup time and made collaboration between teams much easier. Review collected by and hosted on G2.com.