Tools for machine learning model management

Dotscience makes data science teams more productive, by enabling collaboration, flexible access to high performance compute, and version control.

Which AI Maturity Level Are You At?

Image for Level 1: Playing with AI

Level 1: Playing with AI

  • PoC projects
  • Models in development but not in production
  • Data science team small or outsourced
Image for Level 2: Unsafely deploying

Level 2: Unsafely deploying

  • Deploying models into production
  • Chaotic working practice doesn’t scale with team
  • Missing or incomplete audit trail
Image for Level 3: Trusted AI

Level 3: Trusted AI

  • Model indentity & provenance, audits not a problem
  • Model health monitoring throughout lifecycle
  • Collaboration in place to allow team to scale safely

Dotscience delivers Trusted AI on Day 1

Diagram showing the Dotscience development flow

Dotscience brings order to the chaos

Model health tracking across AI lifecycle for 100x better models. Concurrent collaboration for 100× smoother teamwork. Simplified workflows across development & production for 100x faster time to insight. Track versions & provenance of every digital asset for 100× stronger governance.

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Dotscience products are open and interoperable. They integrate with broader model development and deployment pipelines, to bring governance and collaboration into your end-to-end process.

6 pillars of differentiation

  • Version control Omni Version Control: Track data, code, metrics & models together for stronger governance.
  • Provenance Provenance: Automatically track provenance of every digital asset from decison to data.
  • Metrics Metrics: Model health tracking across AI lifecycle for better models.
  • Collaborate Collaborate: Concurrent collaboration enables smoother teamwork.
  • Runners Runners: Get simplified DevOps for ML, reduce time to insight to minutes.
  • Deploy Deploy: Easily tag, manage and deploy your models to any CI/CD system.
Diagram showing the Dotscience integrated workflow

Onmi Version Control

Simply version every notebook, data file, and model in your AI project.

The Dotscience Python library allows simple annotations of notebooks & Python scripts. Unlike git based version control, the versioning happens inline, and automatically, and doesn't interrupt creative experimentation. Never lose a model again

Provenance

For any version of any asset: code, data or model, track the provenance of that asset.

Provenance works recursively, so you can see the outputs that were fed as inputs to each pipeline stage. Provenance graph is built dynamically, no need to define the DAG up-front.

Metrics

Track parameters and summary statistics such as accuracy continuously as models are developed.

Multiple users' metrics can be shown on the same chart, allowing never-seen-before collaboration between data scientists. Unlike competitive products, metrics are tied strongly back to provenance & model, code & data version history.

Collaboration

Project owners can add collaborators easily through the Settings Panel.

Collaborators see a read-only view of the project until it is forked. When forked, projects can be edited in Jupyter. When improvements to data, notebooks & metrics are made, open a Data Science Pull Requests to propose the change.

Runners

Move AI workloads effortlessly across any infrastructure.

Start out working on your laptop, then do some testing on a GPU enabled cloud instance, and then deploy to your on-prem GPU boxes for full training. Often this would require support tickets and conversations with the ops department. With Dotscience it's built in.

Deploy

Get your models into production where they can start helping users.

Your models can only do so much good on your laptop, yet deploying models to production often requires lengthy interactions with other teams. Dotscience plugs straight into your CI/CD tooling, providing one–click deploys.