Join us June 22 for a live Evidently demo and Q&A. Register now ⟶

Blog posts by #tag

#AMA
#batch-monitoring
#code-example
#company-news
#concept-drift
#data-drift
#data-quality
#events
#ml-model-quality
#ml-model-retraining
#ml-testing
#mlops-q&a
#product-news
#real-time-monitoring

Evidently 0.2.2: Data quality monitoring and drift detection for text data

January 26, 2023

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

December 8, 2022

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

November 29, 2022

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

Evidently

How to contribute to open source as a Data Scientist, and Hacktoberfest 2022 recap

November 1, 2022

Now that Hacktoberfest 2022 is over, it’s time to celebrate our contributors, look back at what we’ve achieved together, and share what we’ve learned during this month of giving back to the community through contributing to open source.

Community

Evidently 0.1.59: Migrating from Dashboards and JSON profiles to Reports

October 31, 2022

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

July 7, 2022

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.

April 6, 2022

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

Evidently 0.1.35: Customize it! Choose the statistical tests, metrics, and plots to evaluate data drift and ML performance.

December 13, 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.

Evidently

Evidently 0.1.30: Data drift and model performance evaluation in Google Colab, Kaggle Kernel, and Deepnote

November 12, 2021

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

October 25, 2021

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

June 4, 2021

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

Evidently

Evidently 0.1.6: How To Analyze The Performance of Regression Models in Production?

February 2, 2021

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

December 29, 2020

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

November 30, 2020

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.

Evidently

ML in production newsletter

Get a roundup with the best blogs, links and events every month. No spam.

Thank you! Please check your email to confirm subscription!
Oops! Something went wrong while submitting the form.

By signing up you agree to receive emails from us. You can opt out any time.

Privacy policyTerms of service
© 2023 Evidently AI
By clicking “Accept”, you agree to the storing of cookies on your device to enhance site navigation, analyze site usage, and assist in our marketing efforts. View our Privacy Policy for more information.
DenyAccept