IBM introduces CodeFlare, an open-source framework for ML apps
With an objective to simplify the combination and scaling of huge information and AI workflows onto the hybrid cloud, IBM has introduced CodeFlare, an open-source, serverless framework designed to simplify the combination and environment friendly scaling of huge information and AI workflows onto the hybrid cloud.
CodeFlare, the platform is an an open-source framework for simplifying the integration and efficient scaling of big data and AI workflows onto the hybrid cloud built on top of Ray, an emerging open-source distributed computing framework for machine learning applications.
CodeFlare is constructed on-prime of a rising open-source distributed computing framework for machine studying purposes often known as Ray. The goal of the new framework is to unify pipeline workflows across multiple platforms without requiring data scientists to learn a new workflow language.
CodeFlare pipelines run with ease on IBM’s new serverless platform IBM Cloud Code Engine, and Red Hat OpenShift. It allows users to deploy it just about anywhere, extending the benefits of serverless to data scientists and AI researchers.
With CodeFlare, IBM aims to give data scientists richer tools and APIs that they can use with more consistency, allowing them to focus more on their actual research than the configuration and deployment complexity, according to the company.
IBM will continue to evolve CodeFlare to support increasingly more complex pipelines. The company is planning on providing enhanced fault-tolerance and consistency, as well as improving integration and data management for external sources, and adding support for pipeline visualization.
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