Evidently

We are launching Evidently Cloud, a collaborative AI observability platform built for teams developing products with LLMs. It includes tracing, datasets, evals, and a no-code workflow. Check it out!

Evidently open-source Python library now supports evaluations for LLM-based applications, including RAGs and chatbots. You can compare, test, and monitor your LLM system quality from development to production.

Did you miss some of the latest updates at Evidently open-source Python library? We summed up a few features we shipped recently in one blog.

Evidently 0.4 is here! Meet a new feature: Evidently user interface for ML monitoring. You can now track how your ML models perform over time and bring all your checks to one central dashboard.

Meet the new feature: data quality monitoring and drift detection for text data! You can now use the Evidently open-source Python library to evaluate, test, and monitor text data.

We are thrilled to announce our latest and largest release: Evidently 0.2. In this blog, we give an overview of what Evidently is now.

In this series of blogs, we are showcasing specific features of the Evidently open-source ML monitoring library. Meet NoTargetPerformance test preset!

In Evidently v0.1.59, we moved the existing dashboard functionality to the new API. Here is a quick guide on migrating from the old to the new API. In short, it is very, very easy.

Meet the new feature in the Evidently open-source Python library! You can easily integrate data and model checks into your ML pipeline with a clear success/fail result. It comes with presets and defaults to make the configuration painless.

Meet the new Data Quality report in the Evidently open-source Python library! You can use it to explore your dataset and track feature statistics and behavior changes.

We are building an open-source tool to evaluate, monitor, and debug machine learning models in production. Here is a look back at what has happened at Evidently AI in 2021.

Now, you can easily customize the pre-built Evidently reports to add your metrics, statistical tests or change the look of the dashboards with a bit of Python code.

Now, you can use Evidently to display dashboards not only in Jupyter notebook but also in Colab, Kaggle, and Deepnote.

You can use Evidently together with Prometheus and Grafana to set up live monitoring dashboards. We created an integration example for Data Drift monitoring. You can easily configure it to use with your existing ML service.

Now, you can use Evidently to generate JSON profiles. It makes it easy to send metrics and test results elsewhere.

You can now use Evidently to analyze the performance of classification models in production and explore the errors they make.

You can now use Evidently to analyze the performance of production ML models and explore their weak spots.

Our second report is released! Now, you can use Evidently to explore the changes in your target function and model predictions.

We are excited to announce our first release. You can now use Evidently open-source python package to estimate and explore data drift for machine learning models.