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Simplify deployment of models built on Nia Platform

After data scientists have developed a AI/ML based model, getting the model into production remains one of the biggest hurdles for the data scientists today. There are multiple challenges to get models into deployment like multiple infrastructure compatibility challenges and the inability of the model to meet the peak demand from business perspective. The model needs to be continuously monitored and needs to meet governance and compliance requirements. Most of the times, these challenges are afterthoughts for data scientists who want to focus on model development.

Nia ModelOps will be available as an integral component of Nia platform to address these pain points for the data scientists. Nia ModelOps integrates seamlessly with key Nia components to deploy, orchestrate and monitor models in either in-house or cloud base deployments. It provides tools to track various AI assets and resources used to build the models and for versioning them for audit and compliance purposes. It has rich in-built dashboard to view end-to-end model work flow and monitor models in either training or production stages. The visual dashboard helps in monitoring model performance, degradation and data drift.

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  • Seamless integration with Nia components
  • Deploy model to any production environment easily
  • Monitor models for performance and drift
  • Meet governance and compliance requirements

Typical Industry Challenges

  • Model deployment remains biggest challenge for data scientists
  • Need integrated tools to support workflow from experimentation to deployment
  • Increasing governance and audit requirements
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How Nia ModelOps Can Help

  • Easily move from experimentation to production stage
  • Built-in work flow for audit, compliance purposes
  • Monitor models for potential issues during deployments