data science
Why Docker Matters for Data Science
Docker containers make data science projects portable and reliable, eliminating version conflicts and missing libraries and making it easy for teams to share and run data science projects in the exact same ...
Best of 2024: AI Emerges as Next Major Kubernetes Challenge
AI is a dominant theme of KubeCon, as data science teams encounter complexity challenges as cloud-native application developers ...
AI Emerges as Next Major Kubernetes Challenge
AI is a dominant theme of KubeCon, as data science teams encounter complexity challenges as cloud-native application developers ...
What Data Scientists Should Know About Kubernetes
Kubernetes is the most widely used platform for managing containerized applications. It’s open source, portable and powerful. A tremendous advantage of Kubernetes is its ability to create and scale containers automatically. Due ...
Red Hat Previews Data Science Service on OpenShift
A Red Hat OpenShift Data Science cloud service for Red Hat OpenShift platforms is now available as a field trial. Will McGrath, principal architect for Red Hat OpenShift Data Science, says Red ...
Efficient MLOps in a Kubernetes Environment
MLOps addresses the specific needs of data science and ML engineering teams without impacting Kubernetes If your organization has already started getting into machine learning, you will certainly relate to the following ...
Domino Data Lab Brings Data Science Platform to Kubernetes
Domino Data Lab is making the case for a multi-cloud approach to building and deploying applications infused with machine learning algorithms now that its platform runs on Kubernetes. Company CEO Nick Elprin ...
