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Machine Learning Tracking

Automatically track and store the entire ML lifecycle from research through production

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Machine Learning Tracking

Automatically track models

  • Tracking ML experiments is automated for easy reproducibility and monitoring
  • Gain real time updates on input parameters and metrics 
  • Reproduce models in one click with stored compute, Docker images and other metadata
  • Monitor CPU, memory, output data and commits of every experiment you run
Automatically track models
Create custom dashboards ​

Create custom dashboards

  • Visualize hundreds of experiments at once to monitor status, accuracy and duration
  • Easily filter out underperforming models and save resources for champion experiments
  • Compare experiments side by side to help you select the best model for your problem
  • Enhance model analysis with native TensorBoard integration and other open source tools
Create custom dashboards ​

Integrate your own code seamlessly

  • Tracking ML experiments and research is easy with Intel® Tiber™ AI Studio research assistant  – no dependencies required
  • Build advanced dashboards with Intel® Tiber™ AI Studio’s Python SDK
  • Track models that run locally or on remote servers
Integrate your own code seamlessly