Now IBM wants to speed up automated transfer learning

IBM introduced CodeFlare at the Ray Summit in June of 2021. The platform was introduced to drastically reduce the time required to set up, run, and scale machine-learning tests. For example, CodeFlare reduced the time to execute each pipeline from 4 hours to 15 minutes when one user used the framework to examine and improve approximately 100,000 pipelines for training machine learning mode. Recently, IBM announced that CodeFlare significantly reduces the time to automate transfer learning tasks for foundation models.

What is CodeFlare?

CodeFlare is a hybrid multi-cloud platform that streamlines the integration, scalability, and acceleration of complicated multi-step analytics and machine learning pipelines. It is an open-source framework that makes it easier to integrate and scale big data and AI operations to the hybrid cloud. CodeFlare is developed on Ray, an open-source distributed computing framework for machine learning applications. Ray’s capabilities are expanded by CodeFlare, which adds specialised aspects that make scaling operations easier.

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