How Can IT Support Emerging Data Science Initiatives?

This week, Omri Geller, Run:AI’s CEO and cofounder spoke on a webinar about supporting the data science lifecycle. His talk centered on the three areas where IT plays an important role in the lifecycle of data science:

  1. MLOps – MLOps is an emerging trend inside the enterprise where DS and IT professionals work together across the data science lifecycle to automate and bring ML algorithms into production.
  2. Kubernetes – IT admins are already embracing K8s to enable using a single IT environment to serve all users whether they are developers or researchers, using CPUs or the new accelerators such as GPUs.
  3. Virtualizing AI Infrastructure – Today, CPUs, storage and networking are virtualized, while AI accelerators and GPUs are not. They’re often deployed in data centers as bare metal and are allocated statically to data scientists. Virtualization of the AI infrastructure is mandatory in order to build a scalable, automated environment for DS experimentation.

You can watch the full webinar – Supporting Emerging AI, ML and the Data Science Lifecycle recording below. Many thanks to our friends at Actual Tech Media who bring together some of the best content for IT practitioners.


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