Machine Learning Workbench

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

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