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Introducing Kaptain

Kaptain, our enterprise MLOps solution, helps you answer the question: how do I get a return on my AI investments? With a familiar environment for development in Kubeflow, plus all other technologies needed to deploy and scale models in production, you can get results faster with Kaptain. And safely as well: we address all critical CVEs in our components. 

Kaptain is seamlessly integrated, tested, and deployed via helm in multiple different environments: public clouds, on-premise, edge, hybrid or multi-cluster with EKS and AKS, and air-gapped environments. Additionally, Kaptain is rigorously tested on Nvidia GPUs and Nvidia DGX boxes.

Kaptain’s Features and Benefits



Out-of-the-box integration of Spark

No need to install additional libraries to create data pipelines or train Spark ML models on multiple CPUs or GPUs

Fully tested pre-baked notebook images

A familiar environment that has been fully tested and integrates with all the shared resources (CPUs, GPUs) and data access controls needed to build and share models as a team

Train, tune, and deploy from a Jupyter notebook

No context switching or credentials and CLI tools on individuals’ laptops

Enterprise-grade security controls, profiles, and identity provider support

Multi-tenancy? No problem!

MLflow experiment tracking

Utilize MLflow’s robust metadata tracking within Kubeflow to get the most out of your experiments

Garbage collection

Configure automatic cleanup of completed and idle workloads created by Kaptain components or the Kaptain SDK

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