Why ML Works?

With the current business challenges and uncertainty, it has become imperative that AI/ML models driving an organization’s business decisions continue to do so effectively. In today’s ever-changing landscape, model and data drift are both very real, and AI/ML models thus need to be monitored on a real-time basis to ensure they stay relevant. When ML models fail in live production, not only do they require valuable data scientists time to fix and redeploy them, they also disrupt the organization’s day to day operations and put it at risk of lapsing on regulatory requirements.

The ML Works accelerator provides customers with a visual provenance graph for an end to end model visibility and pipeline traceability, as well as persona-based dashboards to make real-time model monitoring easy for all personas from Data Engineers to business users. It also allows continuous monitoring of production models for accuracy and relevance, with auto-triggered alerts in the event of model and data drift.



Visual Provenance graph for an end to end model visibility and pipeline traceability, allowing easy troubleshooting of production issues and root cause analysis


Relevant metrics for Business users, Data scientists, ML engineers, and Data engineers, with a persona-based monitoring journey to make monitoring easy for all personas

Model and Data
Drift Analysis

Analysis of data and model drift with auto-triggered alerts, enabling continuous monitoring of production models for accuracy and relevance


Provenance review from dashboard metrics all the way back to base models, ensuring full visibility into model operations, including training data

and Support

Centralized access control, traceability and audit logs to manage multiple user personas & ensure regulatory compliance across platforms, as well as maintenance and uptime governance through an SLA driven response and resolution process

Platform-Agnostic Advisory and Development

Cross-platform and open source setup and support models to cater to myriad business requirements


Pavan Nanjundaiah

Head of Product Engineering

Sunil Ranganathan

Product Head

Speak to Our ML Works Experts