iTuring MRM
Model Risk
Management
Comprehensive governance framework to control
and manage AI model Life cycle
Predict failure of Machine Learning models before it happens
When models fail, it can be a daunting time-consuming task to re-create the whole scenario and understand the root cause of failure. Lack of understanding business expectations, not following the best practices of Data Science and Machine Learning or simply due to a shift in customers’ behavior that may generate newer data points could be few of the reasons. Documenting them in an automated, audit-ready way can be an additional burden. Identifying and calculating drift in - Data, Features and Process to re-train the model requires a lot of manual effort and time.
MRM - Early warning indicator for model failure
With CyborgIntell’s MRM, you can now measure drift in quality of Data, Features, and Efficacy. Make accurate drift assessments and compare with baseline-data used for building the model. Getting to the root cause has never been easier, with all data-baselines being readily available, and drift measured in real time to proactively prompt stakeholders of a probable failure in model performance. With automated learning you can now approve for an incremental, partial, or complete retrain of the model.
Predict Assess Retrain your Data Science Projects
Model risk Identification
With MRM, CI’s model relevancy score helps identify failure of models in advance. Model failure signals are available in real time on a single dashboard displaying drift in Data, Process and Algorithm.
Model Risk Assessment
MRM uses base-line data to assess current performance of model. It identifies, assesses root cause of drift to understand breach in risk thresholds. MRM can be a very powerful tool for monitoring health of all AI/ML models in the enterprise. Continuous monitoring of models can help you make faster decisions and recalibrate the model and deliver expected results to businesses.
Model Risk Governance
MRM will help you to enable governance, accountability through right checks on decisions to retrain models. Decision making requires a high degree of confidence in model results which can only happen through transparency and explainability. With MRM, every model failure, drift in data, process, and features are automatically documented to give you a comprehensive auditable document. Every action performed by AI is time-stamped, detailed, exhaustive and transparent.
Model Monitoring
MRM provides data scientists with very critical information regarding model performance in real-time. The model performance tracking can be done with or without feedback. With feedback would mean inclusion of tools like AUC, Gini, KS, Brier score, Gain, Precision analysis. Without feedback means CI model relevancy score, PSI, CSI, Deviation Score.
Model Risk Mitigation
MRM enables you to set up periodic and automated retraining and recalibration of models. With its advanced features like adaptive intelligence the user has the option to decide on incremental, partial or complete retrain.
Model Serving
MRM assists you to setup training and validation data sets to establish performance and drift analysis. It enables deployment of models in production and risk management framework for continuous monitoring and early warning indication for any model failures.
How MRM can benefit your business
- Enhanced model observability and monitoring
- Early warning indicators of model failure
- Set up notifications or alerts based on predefined thresholds
- Proactively detect anomalies or performance degradation
- Faster root cause analysis - Capture detailed model execution flow, intermediate results and decisions at different stages
- Decide whether to retrain the model incrementally, partially, or completely
- Leverage intuitive charts, graphs and dashboards to visualize data and logs collected
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