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.
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.
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.
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.
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.
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.