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scikit-learn Integration

cnvrg allows you to instantly connect scikit-learn to automate your work and accelerate your development

Flexible ML Platform


Use any language, AI framework, and compute environment. Integrate and
 version any kind of data to reuse in any project, experiment, and/or notebook 

Interactive ML Platform


Use any development environment like JupyterLab, RStudio, and more with pre-installed dependencies and version control

Unified ML Platform


One unified environment to manage, build and deploy your machine learning with all your favorite data science tools

Learn more about the scikit-learn integration

scikit-learn, SKLearn is a free Python machine learning library featured various classification, regression and clustering algorithms including support for vector machines, random forests, and gradient boosting. It is designed to interoperate with the libraries NumPy and SciPy.

Connect cnvrg & scikit-learn in minutes

Accelerate your AI development easily connecting cnvrg & scikit-learn

One-click development environment

  • Get started quickly with out-of-the-box scikit-learn integration with pre-built containers
  • Speed up development with container-based ML platform
  • Leverage advanced algorithms with an easy to use ML workspace and the AI library
Machine Learning Tracking
Machine Learning Pipelines

Production ML at Scale

  • Scale your usage of scikit-learn across hundreds of nodes
  • Effortlessly go from small to big data with an auto-managed and scalable cluster infrastructure
  • Flexibility to run locally or in your own compute resources
Machine Learning Pipelines

Extendable Machine Learning Platform

  • Build and attach your own docker containers with a fully containerized data science platform
  • Connect private or third-party container registries (Docker Hub, NGC) in a few clicks
  • Access the industry’s best ML containers released periodically by the solutions team
Dataset Management