D2iQ has launched KUDO for Kubeflow to simplify and accelerate machine learning (ML) deployments on Kubernetes. An enterprise-ready distribution of open source Kubeflow, D2iQ KUDO for Kubeflow is said to accelerate time-to-market for ML workflows.
KUDO for Kubeflow comes bundled with other ML tools such as Spark and Horovod.
Chandler Hoisington, SVP Engineering and Product, D2iQ, explains: “D2iQ KUDO for Kubeflow enables organizations to develop, deploy, and run entire ML workloads in production at scale, while satisfying security and compliance requirements. This enables data scientists and ML engineers to run their entire ML stack with much higher velocity on Kubernetes infrastructure.”
KUDO for Kubeflow empowers organizations with a platform that provides standardized best practices and tools for running machine learning on Kubernetes, the company said.
By removing the complexity of setting up ML development and production environments, KUDO for Kubeflow is said to enable organizations to improve the productivity of data science teams at a much lower cost.
Data scientists can leverage GPUs and MLOps to speed up the process of training, tuning and deploying models, regardless of the underlying infrastructure, reducing the costs and risks associated with manual setups. ML engineers can now deploy and train ML models at scale, all on a single platform.