Dotscience DevOps For ML Certification.

Audit your current AI initiatives to establish your company's AI maturity level.

Which AI Maturity Level Are You At?

Image for 1. Playing with AI

1. Playing with AI

  • PoC projects are proving that AI can be applied to business problems
  • Models in development but not in production
  • Data science team small or outsourced with manual collaboration
Image for 2. Unsafely deploying

2. Unsafely deploying

  • Deploying models into production
  • Initial wins with models but not yet scaling, and team working inefficiently and treading on each others' toes
  • Missing or incomplete audit trail and minimal production monitoring
Image for 3. Operationalized AI

3. Operationalized AI

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

How Production–Ready is your AI process?

We will also evaluate your current capabilities against the DevOps for ML Manifesto and provide a report. Here are some sample excerpts:

Low Scores on the Certification
  • Poor reproducibility: ad-hoc work done with occasional version control for code, no version control for data or models
  • Poor accountability: difficult to track back from a model to how it was created or to see on what basis it made decisions
  • Manual collaboration: ad-hoc collaboration done in meetings, by sending each other notebooks, or folder structures
  • Manual lifecycle: manual process to deploy model to production, no monitoring of model once in production
High Scores on the Certification
  • Perfect reproducibility: every run of every data transformation or model training can be reproduced, even years later
  • Strong accountability: every human decision that went into creating a model can be forensically reproduced
  • Seamless collaboration: the status of every experiment is seamlessly synchronized to the team's ML knowledge base
  • Continuous lifecycle: automated deployment of models to production with statistical monitoring of model drift

Certification Pricing

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