New open source platform for machine learning on Kubernetes hits
|Richard Harris in Artificial Intelligence Tuesday, February 6, 2018|
Machine learning deployment has many challenges but the new Seldon Core intends to help with it's new open source platform for deploying machine learning models on Kubernetes.
Traditional infrastructure stacks and devops processes don’t translate well to machine learning, and there is limited open-source innovation in this space, which forces companies to build their own at great expense or to use a proprietary service. Data engineers with the necessary multidisciplinary skillset spanning ML and ops are very scarce. These inefficiencies cause data scientists get pulled into quality-of-service and performance-related challenges that takes their focus away from where they can add the most value - building better models.
Data scientists are freed to focus on creating better models while devops teams are able to manage deployments more effectively using tools they understand.
Features of the Seldon Core platform include:
- Enable data scientists to deploy models built with any machine learning toolkit or programming language. We plan to initially support Python-based tools/languages including Tensorflow, Scikit-learn, Spark and H20.
- Expose machine learning models via REST and gRPC automatically when deployed for easy integration into business apps and services that need predictions.
- Handle full lifecycle management of the deployed model with no downtime, including updating the runtime graph, scaling, monitoring, and security.
Read more: https://github.com/SeldonIO/seldon-core
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