# 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. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://eigenmaps.gitbook.io/eizen-observability/why-eizen-observability.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.