Evidently AI is an open-source ML evaluation, testing, and monitoring tool designed for data scientists and ML engineers. It provides a platform for evaluating, testing, and monitoring ML models from validation to production, covering various types of data including tabular, NLP, and LLM.

The platform offers several key features to facilitate the evaluation and monitoring process. Evidently Open-Source is a Python library that enables ML monitoring, allowing users to track and analyze the performance of their ML models. Evidently Reports provide instant visualization of ML models and data, giving users a comprehensive view of the quality of their models. Evidently Test Suites offer structured checks for data and models, allowing users to validate their pipelines and ensure data integrity. Evidently ML Monitoring serves as a centralized hub for ML in production, providing visibility into all production ML models and datasets in a single platform.

Evidently also offers evaluations in different areas, including Data Quality, Data Drift, ML Model Performance, and NLP and LLM. Users can monitor data quality and integrity, track data distribution shifts, evaluate ML model quality and errors, and monitor text-based models and data.

The platform provides extensive learning resources, including a blog with insights on doing ML in production, tutorials on ML monitoring and MLOps, guides that provide in-depth explanations and guidance on MLOps, and special projects that cover ML platforms and ML use cases. There is also an MLOps course and an open-source ML observability course for users to further develop their skills.

Evidently Cloud is the cloud version of the platform, offering a centralized platform to get visibility into all production ML models and datasets. The cloud version is easy to set up and provides a seamless user experience.

In summary, Evidently AI is an open-source ML evaluation, testing, and monitoring platform that provides data scientists and ML engineers with the tools and features they need to evaluate and monitor ML models in production. It offers a range of features, evaluations, resources, and learning materials to support the ML monitoring process in various scenarios.