Kaptain DocumentationKaptain is a cloud-native suite of best-of-breed open-source technologies that allow data scientists to extract value from data immediately by providing a familiar environment for development and all the technologies needed to deploy and scale models in production. Kaptain solves a key problem that enterprises face: How to get a return from your expensive AI investments? Promoting from prototype to production is often hard, but it does not have to be in this case, with Kaptain.
D2iQ’s Kaptain leverages our expertise in Kubernetes, so that companies can run their machine learning workloads anywhere: in the cloud, on-premise, or in hybrid environments. Kaptain is an opinionated distribution based on Kubeflow: everything you need to train, deploy, and scale models is packaged and tested, so you can rest assured that it works out of the box.
If you want to learn more, please read our blog post for Kaptain.
Kaptain’s Features and Benefits
| Features | Benefits |
|---|---|
| Out-of-the-box integration of Spark and Horovod | 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 and profiles | Multi-tenancy? No problem! |