Why Eizen Observability?

Monitor Models Performance

Machine learning engineers need to continuously monitor the model's performance to ensure it is meeting the desired accuracy and error thresholds.

Detect Data Drift

The data distribution in production may be different from the distribution of the data used to train the model. This can lead to poor model performance and require frequent retraining.

Explain Predictions

Explainability helps ML Engineers understand how a model is making its predictions, which can reveal areas of the model that are performing poorly or introduce bias.

Optimize Models Performance

Improve Model performance by automating the retraining process. Our AGI system will create a dataset with retrainable points and automates the retraining process.

Identify Model Bias

Explainability techniques can reveal bias in a model's predictions, allowing ML engineers to identify and address the sources of bias.

Build Trust

Model Explainability can help build trust by providing transparency and justification for the model's decisions, which makes it easier to understand the model's predictions and to use the model in decision-making.