Validation Tool for Your Machine Learning Models

Gain confidence in the models developed by your dedicated data teams or consultants before using them in production.

  • Actionable Insights
  • Hassle-Free Process
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Make sure your models are doing what they should and detect anomalies before they affect your business.

01.

Detect Bias and Validate Your Features​

Detect bias in your models ahead of time and make sure that the proper features are being considered when generating a prediction.

Identify data drift as your model is used for real business decisions to ensure that the predictions driving these decisions remain as accurate as possible.

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02.

Anticipate and Solve Potential Problems

Identify poor labeling, over and under-fitting in your model and understand how well your model will perform when confronted with real-world data.

Quickly detect data leakage, noise sensitivity and vulnerability to extreme scenarios.

03.

Improve Your Model's Performance

See opportunities for simplifying your model or pruning features that have little or no impact on model accuracy, allowing you to gain improved performance and reduced operating costs.
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04.

In-place compatibility

Compatible with any trained TensorFlow model, no need to migrate your entire data pipeline to a new platform.

Validate your models from AWS, GCP, Azure or your own environment.

05.

Clear and Understandable Report

Get a complete report, tailored for business stakeholders, with all pertinent validation details that can be preserved for compliance and regulatory requirements.
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Model Validation FAQ

How does it work?

Our machine learning model validation tool takes a trained model, the training dataset and the validation dataset, and performs a series of mathematical validations on all these items.

By doing so, it can detect many potential issues with a model that would prevent it from performing at peak efficiency in the desired business scenario. Since underperforming models can directly cause loss of efficiency and increase costs, this can be a major issue.

Robustness vs Accuracy?

Model robustness is a large term that encompasses many characteristics that predict how well a model will perform in real-life scenarios. Typically, when training a ML model, data scientists focus on improving a single metric: accuracy.

Accuracy measures how well the model can accurately perform the right prediction given the training and validation sets. However, this leaves the model vulnerable to a host of other shortcomings.

Our machine learning model validation tool seeks to address these shortcomings by giving visibility to more than just accuracy and allow business shareholders to deploy ML models into production with confidence.

  • Feature Bias
  • Labeling Errors
  • Data Leakage
  • Over-fitting
  • Under-fitting
  • Model Simplification
  • Feature Discrimination/Pruning
  • Sensitivity to random noise
  • Sensitivity to extreme noise
  • Data Drift

What's included in the model validation report?

The report outlines all the outputs described in the previous section and is tailored for business stakeholders. While it contains some raw data that can help Data Science teams pinpoint and fix issues with their model, it will also contain a clear explanation of each of the observations as well as potential impacts that this could have on model performance.

The report is also signed digitally and timestamped. It can be preserved for compliance and regulatory requirements.

With the report in hand, you will be able to confidently deploy your machine learning models into production and ensure the best possible business outcomes from using them.

Validate Your Models Today!