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@katanaml

Katana ML

katanaml
Worldwide

Designed for ML services

Many ML projects are implemented with Jupyter notebooks. It is really hard to scale and maintain such solutions when deployed to production. We recommend investing in MLOps early. Develop your ML solution with Skipper to be ready for production deployment.

Data processing - Data is processed in a separate container, this makes it easier to apply future changes.

Prediction service scaling - Prediction service is configured to run in separate Kubernetes Pod for better scalability.

@katanaml

Reaching this goal will help us to evolve and improve our open-source ML products.

Meet the team

Featured work

  1. katanaml/katana-skipper

    Simple and flexible ML workflow engine

    Python 382

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