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Machine Learning Workbench

Collaborate, build and manage ML models from experimentation to production in one unified environment

Machine Learning Workbench

Unified Code-first Data Science Stack

The ML Workbench is a code-first ML platform intelligently designed for data scientists to research, build, and collaborate on projects independent of DevOps. Enhance productivity with container-based model management, MLOps automation, and end to end tracking and monitoring for easy reproducibility.

Accelerate ML Workflows with end-to-end MLOps

  • Simplify engineering heavy tasks like tracking, monitoring, configuration, compute resource management, serving infrastructure, feature extraction, and model deployment
  • Instantly launch any ML or DL framework in one click with native integration to NVIDIA GPU-optimized Containers
  • Launch pre-configured workspaces with any on-premise, cloud or hybrid compute engine and instantly scale with native Kubernetes, and Spark
  • Rapidly run hundreds of experiments, ML jobs and pipelines simultaneously

Extend Flexibility and Scalability with Custom Interactive Workspaces

  • Launch any interactive workspace environment with built-in support to Jupyter, RStudio, VSCode, and more with pre-installed dependences and version control
  • Dynamically attach data, sync files and artifacts with continuous tracking of work
  • Centralize info, logs and metadata with easy access to TensorBoard, SparkUI and all your favorite tools with one data science platform
  • Deploy production-grade endpoints directly from your workspace environment

Improve Productivity with Collaborative Notebooks, Models and Dashboards

  • Track, visualize and share all ML notebooks in your workspace automagically
  • Share and reuse ML components to build fast, reproducible ML pipelines
  • Enable end to end model management with hyperparameter optimization, experiment tracking, comparing
  • Quickly publish interactive dashboards using Dash, Voila, or RShiny for improved reporting