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While the SaaS experience does a lot to make data science teams productive, cloud native platforms can’t deploy where enterprise teams actually work. This leaves organizations to sort it out on their own.

Recently, a “container native” approach has emerged that combines flexible deployment across machines with automation & streamlining for end users. This containerized & decentralized approach is native to hybrid infrastructures and goes beyond just reducing the pains to helping teams realize the full potential of hybrid infrastructures.

Pretty much every enterprise needs a data science platform that:

1. Provides productivity and collaboration features for individuals and teams;
2. Streamlines the creation & distribution of Python and R environments;
3. Lightens the technical load on data scientists and IT staff;
4. Runs across bare metal, private cloud, and public clouds.