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Connecting Evidently AI with Machine Learning Pipelines

Evidently AI is a valuable tool for monitoring your machine learning models and data, but to fully leverage its benefits, you need to seamlessly integrate it into your existing machine learning pipelines.  This article outlines how to connect Evidently AI with popular ML pipelines and frameworks.

 Understand the structure and components of your ML pipeline. Identify the stages where you can integrate Evidently AI for data drift monitoring, model performance checks, or data quality evaluation.

  Use the `Report` object provided by Evidently AI. You can create a report object in your pipeline code. This will enable you to generate monitoring reports as part of your pipeline's flow. 

 Define the metrics, presets, and custom checks you want to include in your report.  Adjust the report to fit your specific requirements and monitoring needs. 

 Execute the report within your pipeline workflow.  This will typically happen after your model training or data processing steps, allowing you to analyze the data as it flows through your pipeline.

 Save the report in a format compatible with your pipeline and monitoring infrastructure.  The HTML format is a commonly used option for interactive reports.

  Many popular machine learning libraries and frameworks can be integrated with Evidently AI:

Discover how to integrate Evidently AI with popular machine learning pipelines and frameworks.

Connecting with ML Pipelines

The Evidently AI API provides a powerful way to programmatically interact with Evidently AI's capabilities, allowing you to build custom monitoring solutions, integrate with your existing systems, and automate analysis tasks.  This article will guide you through the essential aspects of utilizing the Evidently AI API.

  First, make sure you have the latest version of Evidently AI installed.

Learn to interact with the Evidently AI API for programmatically accessing and manipulating data.

Using the Evidently AI API

Integrating Evidently AI with Monitoring Tools

Evidently AI provides powerful data and model monitoring capabilities, but you can enhance its effectiveness by seamlessly integrating it with your existing monitoring tools and dashboards.  This integration allows you to centralize monitoring data, streamline workflows, and gain a unified view of your machine learning systems. 

  Select the monitoring tool or dashboard that best suits your needs and existing infrastructure. Some popular options include:

 Determine the data sources from your Evidently AI reports that you want to integrate into your monitoring tool.  This might include metrics like data drift scores, model performance metrics, or data quality statistics. 

  Leverage the Evidently AI API to extract the necessary data from your reports.  You can programmatically query the report object to retrieve the required values.

 Ensure that the data retrieved from Evidently AI is formatted in a way that's compatible with your chosen monitoring tool. This often involves converting data types, structuring the data into appropriate formats, and mapping specific metrics to their corresponding variables.

  Set up dashboards in your monitoring tool that visualize the data from Evidently AI.  Use graphs, charts, tables, or other visualization methods to provide clear insights into the performance and health of your machine learning systems.

  Configure alerts in your monitoring tool to notify you of significant events.  For example, you might set alerts for data drift exceeding a specific threshold, a significant drop in model performance metrics, or issues related to data quality.

    1. **Generate Evidently AI Data:** Use the Evidently AI API to generate your report data as needed, saving the data in a format that's easily readable by Grafana (e.g., CSV or JSON).

    2. **Configure Grafana:** In Grafana, add a new data source for the format you've chosen to store the Evidently AI data.  (Typically you might add a file data source or define a custom data source if necessary). 

    3. **Create Panels:**  Create panels in your Grafana dashboard that query the data source. Visualize your Evidently AI data using line charts, bar charts, or other appropriate graphs. 

    4. **Set Alerts:**  Within Grafana, configure alerts to trigger notifications when certain thresholds are met (e.g., data drift exceeds a set limit, model performance drops below a specified value).

Explore integrations with monitoring dashboards and alerting systems to streamline your workflow.

Integrations & API

Connecting with ML Pipelines

Using the Evidently AI API

Integrating with Monitoring Tools