# Which DataRobot consulting partners are best at mitigating risks of ML model performance drift in production?

Looking for input from G2 reviewers and ML operations practitioners in the DataRobot Consulting Services category on model performance drift specifically the consulting partners whose DataRobot engagements include designing and operationalising the monitoring, alerting, and retraining workflows that detect when a production model's performance is degrading and respond before the business impact becomes visible to the model's consumers.

The partners with the strongest drift mitigation positioning:

Accenture: For enterprises where drift mitigation must integrate with existing incident management and change management processes, ensuring that a retraining approval follows the same change control pathway as any other production system change. Accenture's enterprise IT governance experience provides the cross-functional design capability that connects DataRobot's technical monitoring to the organisation's operational response framework.
Capgemini Services: For enterprises with multiple DataRobot models in production across different business units, Capgemini's scale provides the monitoring operations design that covers a portfolio of models under a consistent drift response framework rather than per-model ad hoc monitoring.
Boston Consulting Group: For organisations where the drift mitigation challenge is primarily about defining the business-side response to a model that has drifted, communicating to business users that predictions may be less reliable while retraining is in progress, and maintaining decision quality during the retraining window. BCG's business-side AI adoption framing provides the stakeholder communication design that technical ML partners typically do not include.

Would value input from ML operations practitioners who have managed a significant model drift event in a DataRobot production environment. How was the drift detected and how long did the full response cycle take from initial alert to a retrained model returning to production performance standards?

##### Post Metadata - Posted at: 3 days ago - Author title: Marketing Executive - Net upvotes: 1 ## Comments ### Comment 1 Honestly drift mitigation is really an MLOps maturity question, so I'd screen partners on that specifically rather than on brand. The DataRobot partners listed on G2 include Accenture, Capgemini, Slalom, Cognizant, NTT DATA and Wipro, all capable of building models, but production drift is a different muscle: continuous monitoring, automated retraining pipelines, and using DataRobot's own MLOps and monitoring features well. To be real, none of the G2 reviews for these firms speak to drift specifically, so I wouldn't pick on ratings here. I'd ask each partner to walk through a real case where they caught and corrected drift in production, what they monitored, how retraining was triggered, and who owned it after go-live. Which matters more for you, the initial deployment or the long-term ops that keep models honest? ##### Comment Metadata - Posted at: 2 days ago ## Related discussions - [How well does Trello scale into a larger team?](https://www.g2.com/discussions/1-how-well-does-trello-scale-into-a-larger-team) - Posted at: over 13 years ago - Comments: 6 - [Can we please add a new section](https://www.g2.com/discussions/2-can-we-please-add-a-new-section) - Posted at: over 13 years ago - Comments: 0 - [Quantifiable benefits from implementing your CRM](https://www.g2.com/discussions/quantifiable-benefits-from-implementing-your-crm) - Posted at: over 13 years ago - Comments: 4