Machine Learning Review and Monitoring

Measure Your Predictive Model Accuracy and ROI

An undisputable challenge while solving business problems using machine learning is that predictive models degrade, and when not properly monitored over time, even the best models can incur more loss than profit. Machine Learning Review and Monitoring (MLRAM), a one-of-its-kind ML operations system, comes to the rescue.

MLRAM provides a visual analysis of various model performance metrics in real-time to enable the ML practitioners to make an informed decision further to improve the model. It is the only solution in the market which converts model accuracy to business value using the proprietary rule-based methodology.

Works with any ML Platform

All the features of MLRAM are available with integrations with virtually all major ML platforms in the market (including AutoML). These integrations range from open-source ML libraries like TensorFlow, Scikit Learn, and PyTorch to Cloud providers GCP, Azure, and AWS.

Some of the benefits are:
  • Derive Business Value:

    The only solution in the market which converts model accuracy to business value using the proprietary rule-based methodology.

  • Cautionary Alerts:

    Notifies stakeholders when model accuracy reaches below acceptable limits

  • Real-time Performance Analysis:

    Provides insights into ongoing changes in the data

  • Save Computation Expenses:

    Saves unnecessary training cycles

Architecture of MLRAM

MLRAM Architecture

MLRAM Analytics Engine

This is the backbone of the MLRAM service. It performs all the intensive computations required for calculating the performance indicators of the predictive model(s).

These computations include calculating accuracy, business value, and return on investment based on proprietary rules. The performance indicators are monitored by the Review Service to trigger the necessary steps to improve the model and/or alert the model administrator.

MLRAM Diagnostic Charts

This component shows the performance of the predictive model(s) in terms of graphs and charts in a more intuitive way for interpreting the various performance indicators.

These charts are updated in real-time while the model is being consumed for predictions.

Review Service

This service continuously monitors the performance indicators of the predictive model and takes cues from the rules set by the model administrator to alert it and/or trigger the model-retraining pipeline.

It further consists of the following main components:

  • Alerting System– This alerts the model administrator for any departure from the expected behavior of the predictive model
  • Model Retrain Service– This service triggers the MLOps pipeline of the ML compute service integrated with the project
  • Rules Engine– This contains the rules set by the administrator for alerting it and invoking the Model Retrain Service
  • Actuals Endpoint– The actuals for every corresponding prediction is fed into the service, which then feeds them into the central database

ROI Based on Revenue versus Investment

Scoring metrics alone tell you nothing about the true success of your predictive model.

MLRAM allows the computation of ROI and business value based on providing simple formulae for revenue and investment.

This saves entire separate analytical programs from being developed.

How to use MLRAM?

You just have to upload the predictions vs actuals data (works for all classification, regression, and time-series predictive models). MLRAM provides a detailed analysis of the performance of the models suited to your business scenario in real-time.

Track Model Accuracy

In this video see how your model performs in the real world by exploring prediction vs. actual accuracy across different time-intervals.

Visualize Model Drift

Examine concept drift as changes in target actual values vs. predicted values at different levels of granularity.
In this video, see data drift as feature values over various timescales.

Monitor and Automate

With powerful monitoring and alerting features, your team is notified when something is wrong. Automatically retrain your model with the latest real-world data based on multiple triggers, reducing cost and impact to your business.

One line of code to integrate

The code here is all you need to do send your actual and predicted values to the MLRAM service, from any application that hosts your predictive model, using any ML platform. You can also upload a spreadsheet of actuals and predictions to the service for immediate results without any code integration.

Pricing Plans

Free Trial

Up to 100 predictions versus actuals per month.



Up to 1000 predictions versus actuals per month.



Up to 10000 predictions versus actuals per month.



For custom pricing please connect with one of our representatives.
All plans include up to 6 months of predictions versus actuals storage. Have questions or special requirements?
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