Evidently AI Blog

Insights on doing machine learning in production.

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Best blogs

Hand-picked selection of our most popular blogs.

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Shift happens: we compared 5 methods to detect drift in ML embeddings

May 17, 2023

Monitoring embedding drift is relevant for the production use of LLM and NLP models. We ran experiments to compare 5 drift detection methods. Here is what we found.

ML Monitoring

Monitoring ML systems in production. Which metrics should you track?

July 18, 2022

When one mentions "ML monitoring," this can mean many things. Are you tracking service latency? Model accuracy? Data quality? This blog organizes everything one can look at in a single framework.

ML Monitoring

Which test is the best? We compared 5 methods to detect data drift on large datasets

June 20, 2022

We ran an experiment to help build an intuition on how popular drift detection methods behave. In this blog, we share the key takeaways and the code to run the tests on your data.

ML Monitoring

How to set up ML Monitoring with Evidently. A tutorial from CS 329S: Machine Learning Systems Design.

June 3, 2022

Our CTO Emeli Dral gave a tutorial on how to use Evidently at the Stanford Winter 2022 course CS 329S on Machine Learning System design. Here is the written version of the tutorial and a code example.

Tutorials

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Tutorials

A collection of in-depth tutorials on ML model evaluation and monitoring. Code included!

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Batch ML monitoring blueprint: Evidently, Prefect, PostgreSQL, and Grafana

May 4, 2023

In this code tutorial, you will learn how to run batch ML model inference, collect data and ML model quality monitoring metrics, and visualize them on a live dashboard.

How to set up ML monitoring with email alerts using Evidently and AWS SES

April 24, 2023

In this tutorial, you will learn how to implement Evidently checks as part of an ML pipeline and send email notifications based on a defined condition.

Featured blogs by category

ML Monitoring

All things ML monitoring, from introductory topics on data and concept drift to architecture deep dives.

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Shift happens: we compared 5 methods to detect drift in ML embeddings

May 17, 2023

Monitoring embedding drift is relevant for the production use of LLM and NLP models. We ran experiments to compare 5 drift detection methods. Here is what we found.

ML model maintenance. “Should I throw away the drifting features”?

October 26, 2022

Imagine you have a machine learning model in production, and some features are very volatile. Their distributions are not stable. What should you do with those? Should you just throw them away?

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MLOps

How to take ML models to production and build efficient operations around it.

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An MLOps story: how Wayflyer creates ML model cards

April 3, 2023

How do different companies start and scale their MLOps practices? In this blog, we share a story of how Wayflyer creates ML model cards using open-source tools.

How to build an ML platform? Lessons from 10 tech companies

February 22, 2023

How to approach building an internal ML platform if you’re not Google? We put together stories from 10 companies that shared their platforms’ design and learnings along the way.

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Community

News and content from the Evidently community.

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AMA with Stefan Krawczyk: from building ML platforms at Stitch Fix to an open-source startup on top of the Hamilton framework

March 19, 2023

In this blog, we recap the Ask-Me-Anything session with Stefan Krawczyk. We chatted about how to build an ML platform and what data science teams do wrong about ML dataflows.

AMA with Neal Lathia: data science career tracks, shipping ML models to production, and Monzo ML stack

February 6, 2023

In this blog, we recap the Ask-Me-Anything session with Neal Lathia. We chatted about career paths of an ML Engineer, building and expanding ML teams, Monzo’s ML stack, and 2023 ML trends.

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Evidently

Latest product releases and company news.

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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.

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.

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