LLM evals + Hacktoberfest = ❤️ Learn how to contribute new LLM evaluation metrics to the open-source Evidently library
Product
LLM observability
Evaluate LLM-powered products, from RAGs to AI assistants.
ML observability
Monitor data drift, data quality, and performance for production ML models.
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Open-source Python library for ML monitoring with 20m+ downloads.
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Evidently
Evidently
Meet Evidently Cloud for AI Product Teams
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
Evidently 0.4.25: An open-source tool to evaluate, test and monitor your LLM-powered apps
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.
Evidently
7 new features at Evidently: ranking metrics, data drift on Spark, and more
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
Evidently 0.4: an open-source ML monitoring dashboard to track all your models
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.
Evidently
Evidently 0.2.2: Data quality monitoring and drift detection for text data
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.
Evidently
Meet Evidently 0.2, the open-source ML monitoring tool to continuously check on your models and 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.
Evidently
Evidently feature spotlight: NoTargetPerformance test preset
In this series of blogs, we are showcasing specific features of the Evidently open-source ML monitoring library. Meet NoTargetPerformance test preset!
Evidently
Evidently 0.1.59: Migrating from Dashboards and JSON profiles to Reports
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.
Evidently
Evidently 0.1.52: Test-based ML monitoring with smart defaults
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.
Evidently
Evidently 0.1.46: Evaluating and monitoring data quality for ML models.
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.
Evidently
7 highlights of 2021: A year in review for Evidently AI
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.
Evidently
Evidently 0.1.35: Customize it! Choose the statistical tests, metrics, and plots to evaluate data drift and ML performance.
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.
Evidently
Evidently 0.1.30: Data drift and model performance evaluation in Google Colab, Kaggle Kernel, and Deepnote
Now, you can use Evidently to display dashboards not only in Jupyter notebook but also in Colab, Kaggle, and Deepnote.
Evidently
Real-time ML monitoring: building live dashboards with Evidently and Grafana
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.
Evidently
Evidently 0.1.17: Meet JSON Profiles, an easy way to integrate Evidently in your prediction pipelines
Now, you can use Evidently to generate JSON profiles. It makes it easy to send metrics and test results elsewhere.
Evidently
Evidently 0.1.8: Machine Learning Performance Reports for Classification Models
You can now use Evidently to analyze the performance of classification models in production and explore the errors they make.
Evidently
Evidently 0.1.6: How To Analyze The Performance of Regression Models in Production?
You can now use Evidently to analyze the performance of production ML models and explore their weak spots.
Evidently
Evidently 0.1.4: Analyze Target and Prediction Drift in Machine Learning Models
Our second report is released! Now, you can use Evidently to explore the changes in your target function and model predictions.
Evidently
Introducing Evidently 0.0.1 Release: Open-Source Tool To Analyze Data Drift
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.
Product
LLM observability
Evaluate LLM-powered products, from RAGs to AI assistants.
ML observability
Monitor data drift, data quality, and performance for production ML models.
Open-source
Open-source Python library for ML monitoring with 20m+ downloads.
Pricing
Docs
Resources
Blog
Insights on building AI products
ML and AI platforms
45+ internal ML and AI platforms
Tutorials
AI observability and MLOps tutorials
ML and LLM system design
450 ML and LLM use cases
Guides
In-depth AI quality and MLOps guides
Community
Get support and chat about AI products
Open-source AI observability course
Sign up now
Get demo
Sign up
GitHub
Get demo
Sign up
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