AI production

Monitor and Measure ROI

Automated strategies to ensure the performance of all generative AI and predictive AI models in production.

Monitor All Generative AI and Predictive AI Models from One Central Location

Understand service health, data drift, and accuracy statistics, schedule monitoring jobs, set custom rules, notifications, and retraining settings.

Get Real Time Insights and Alerts No Matter Where Models are Hosted

Live health monitoring, alerts, and deep production diagnostics let you see exactly which deployments are having issues, regardless of their build origin or deployment destination.

Compare, Challenge, and Replace Models Instantly

DataRobot proactively and automatically suggests challenger models that you can quickly update to prevent production issues.

Maintain ROI of Models in Production

Calculate ROI for complex use cases, then manage and maintain the performance of your deployments over time.

Monitor What Matters with Custom Performance Metric Tracking

Enterprises need to be able to directly tie their generative AI and predictive AI initiatives to top and bottom line impact. DataRobot’s custom inference metrics allow you to build and track business critical metrics and monitor the ROI of each deployment in one central location to ensure their value, even for models running outside of the DataRobot AI Platform. Custom performance metrics such as toxicity scores can be tracked for generative AI models. Likewise, you can track business critical metrics like the cost of an error or LLM cost, to monitor business impact.

Effortlessly Manage Drift and Accuracy

With a suite of drift and accuracy management capabilities, monitoring and maintaining the performance and health of all your models has never been easier. The speed and depth at which you can analyze a shift means you can take appropriate action before the business is impacted. For both generative AI and predictive AI deployments, easily visualize data drift including drift on prompts and completions for LLMs. Just as easily, track accuracy for specific batches of predictions and compare them. Drill down into segments to see which specific trends are driving the overall changes in your metrics.

Challenge Your Models

Don’t let your production models get lazy. Analyze performance against your real-world scenarios to identify the best possible model at any given time. Bring your own challenger models with you or allow the DataRobot AI Platform to create them for you. Then generate challenger insights for a deep and intuitive analysis of how well the challenger performs and how it measures up to the champion. Challenger comparisons can be used for Time Series, multiclass, and external models.

Automate Monitoring Jobs Across all Environments

Free up data science resources and drop your manual pipelines by scheduling monitoring jobs for all generative AI and predictive AI models deployed on-prem or across hyperscalers. Set your own rules for how frequently predictions or actuals should run and when an alert should be generated via the Timeliness Indicator, eliminating the manual work required to maintain models across your ecosystem.

Monitor for Bias and Fairness

By leveraging five different industry standard fairness metrics used to check for model bias, DataRobot gives you a strong defensive strategy and a guided experience to help you determine which fairness metric will be most meaningful to your use case. After deployment, DataRobot AI Production ensures your model isn’t vulnerable to bias over time, with automated alerts to inform you if your model falls below set thresholds. If bias is detected you can leverage insights to help identify the root cause and quickly address it.

Effortless Operational Observability

Our LLMOps and MLOps capabilities give you a 360 degree view of operational activities, alerting and tracking your entire fleet of AI Assets. You can graph and set policies around errors and model latency. This helps you maintain service health, uphold your SLAs, and run robust AI-driven applications. To react and respond when deployments start decaying, create multiple alerts based on chosen thresholds and customized model refresh strategies – take action after an event – like a drop in accuracy or when drift occurs – or on a specific schedule.