Events

Have a question or need help with the course? Join weekly Q&A sessions with the course instructor, Emeli Dral. 

Note: there is no presentation or formal program for the office hours. All the course materials are already available on the 

This event is the opportunity for you to ask any questions you have about the course and its contents.

We will answer questions live on a Zoom call. The event will not be recorded.

There is a 15-minute tolerance for this event, so come on time :) 

Open-source ML observability course. Office hours

 is an open-source Python library for ML monitoring. It helps evaluate, test, and monitor the performance of ML models from validation to production.

​​​Want to know what you can do with Evidently and whether it is the right tool for your needs? Join us for the introductory Evidently Monthly Demo!

We will spotlight the features of the open-source Evidently library and wrap it up with a demo example.

 The link will be available once you register for the event. Please register to join the demo.

Evidently Monthly Demo

​​Want to know what you can do with Evidently and whether it is the right tool for your needs? Join us for the introductory Evidently Monthly Demo!

​Want to know what you can do with Evidently and whether it is the right tool for your needs? Join us for the introductory Evidently Monthly Demo!

Join us for the big Evidently launch event and demo! Our Co-founders, Elena Samuylova and Emeli Dral, will spotlight new features and share the future product vision. 

Live demo example and interactive polls are included 🔥

👩‍💻 Live code demo of new features

 The link will be available once you register for the event. Please register to receive an invite to the calendar and a reminder notification.

 If you are not sure you can join the event live, feel free to sign up anyway: we will send you the link to watch the recording after the event.

Evidently Launch Event & Demo

Want to know what you can do with Evidently and whether it is the right tool for your needs? Join us for the introductory Evidently Monthly Demo! 

We will spotlight the features of the open-source Evidently library and wrap it up with a demo example. 

Join us for the next text-based AMA session with 

 to chat about ML monitoring learnings from 30+ machine learning use cases. 

Lina is a pragmatic ML consultant developing and running ML applications with millions of users. As a freelance ML engineer, she works on various use cases: from predicting traffic problems from tweets to implementing underwriting risk scoring. She likes to solve business problems end-to-end and make machine learning or simple, fast heuristics work in the real world.

Prior to ML consulting, Lina developed real-time, deep learning personalization models for more than 32M users at Zalando and introduced MLOps best practices at the German online bank DKB. 

In her spare time, Lina likes to exchange with other people on how they can implement best practices in machine learning, particularly on the topics of ML monitoring (check out this 

While you can ask Lina ANYTHING, here are a few topics as an example:

​​📊 Building ML monitoring systems

✅ How to prioritize ML monitoring metrics

 #ask-me-anything channel. Here’s how it works:

You can post your questions in advance or ask them live.

 Bear in mind that if we have a lot of questions, we’ll prioritize live questions, though. The author of the best question gets recognition from Evidently. 

to receive an invite to the calendar and a reminder notification. 

If you are not sure you can join the event live, feel free to sign up anyway: 

you can ask your question in the registration form and find the answer in 

Ask-Me-Anything on ML model monitoring w/ Lina Weichbrodt

Join the Evidently Community for the next text-based AMA session with 

 to chat about developing, productionalizing and maintaining ML dataflows.

, an open-core platform for ML dataflows. Before starting his own company, Stefan led the Model Lifecycle team at Stitch Fix, where he focused on building the self-service ML platform for over 100 data scientists (i.e., see blogs on 

, a general-purpose micro-framework for creating dataflows from python functions. 

Prior to Stitch Fix, Stefan was bridging the gap between engineering and ML at Idibon and Nextdoor and worked on content-based recommendations at LinkedIn. He grew up in New Zealand, graduated Stanford with a MSCS, and speaks Polish.

While you can ask Stefan ANYTHING, here are a few topics as an example:

Ask-Me-Anything on ML platforms and dataflows w/ Stefan Krawczyk

 to chat about building ML platforms and high-performance ML teams, scaling MLOps, and more. 

Neal is currently the Staff ML Engineer at Monzo, an online bank based in the United Kingdom. He focuses on building Monzo’s ML platform and ML systems that help the company scale. Formerly, he was the Associate Director of Machine Learning at Monzo.

. It is a Python library allowing to version, export, and save ML models to a filesystem or a cloud storage provider.

Previously, Neal worked on recommender and ranking systems at Skyscanner. He also was a Senior Research Associate in the Computer Lab at the University of Cambridge, working on healthcare mobile apps that use smartphone sensors. 

While you can ask Neal ANYTHING, here are a few topics as an example:

The session will take place on this channel on

Evidently AMA on scaling ML teams and MLOps w/ Neal Lathia

Join us for the biggest Evidently launch yet! Our Co-founders, Elena Samuylova and Emeli Dral, will spotlight the latest features and the future product vision.

Evidently 0.2 is here! We finished a major refactoring and put a lot of learnings into code. 39 releases and over 1.5 million downloads later, we are proud to share what Evidently 

📈 What exactly can you do with Evidently now (a lot!)

Evidently Launch Event

 to chat about deploying ML systems, monitoring ML models in production, AutoML, and more. 

Ben is the Principal Architect at Databricks. He specializes in deploying ML systems at scale, predictive modeling, recommendation engines, and TensorFlow-powered CNNs, utilizing scala on Spark and standalone Python. He also created the 

In his previous life, Ben earned a degree in Nuclear Engineering and worked as a Marine Engineer for US Navy. 

While you can ask Ben ANYTHING, here are a few topics as an example:

1️⃣ You post a question on the channel

3️⃣ The author of the best question gets recognition from Evidently

The session will take place on Discord #ask-me-anything channel.

Evidently AMA on deploying ML systems w/ Ben Wilson

Now that Hacktoberfest is over, it’s time to look back at what we’ve achieved together! Join our informal Hacktoberfest recap call to celebrate every contribution and chat with fellow community members and the Evidently AI team.

✅ A word from Emeli Dral on the new Evidently API

We will not record it, so come join us live!

 channel to chat with fellow contributors.

Evidently Hacktoberfest Recap Call

 to chat about building data pipelines, batch data processing, orchestration, and more. 

, an open-source tool that simplifies building data pipelines. Previously, he worked as a freelance Data Scientist and created 

, an open-source project that became viral with 5,000 + GitHub Stars within the first few weeks of its release. 

Rick studied MSc in Computer Science at TU Delft when he left to create Orchest with Co-Founder 

. He previously earned an MBA from Erasmus University in 2017. 

While you can ask Rick ANYTHING, here are a few topics as an example:

Evidently AMA on batch data processing w/ Rick Lamers

Never contributed to open source before? Join us for tips on how to get started and get your pull request approved. Live coding session included 🔥

This October we invite data scientists to dip their toes into open-source contribution! For Hacktoberfest, we ask contributors to help us add new statistical metrics and tests to detect data drift for production machine learning models. Join our Evidently Hacktoberfest Kickoff Call for contributor guidelines and a live coding session on adding a new data drift test to the Evidently library. 

✅ Evidently Hacktoberfest guide walkthrough

👩‍💻 Live coding session: adding a new data drift test to the Evidently library 

Or explore Evidently Hacktoberfest issues on 

 channel to chat with fellow contributors and get support from maintainers.

Evidently Hacktoberfest Kickoff Call

 to chat about ML at a reasonable scale, RecSys, DataOps, MLOps stack, and more. 

, an open-source project for behavioral testing for recommender systems. He is a proponent of “the reasonable scale ML” and has explored that theme in depth

(i.e., you can start with the article “

He is also an Adjunct Professor of ML at NYU, teaching NLP and ML Systems to Master students. 

Previously, Jacopo was a Director of AI at 

, a SaaS platform for search, recommendations, and personalization, and Co-founder and CTO of Tooso, an NLP startup acquired by Coveo.

While you can ask Jacopo ANYTHING, here are a few topics as an example:

Evidently AMA on ML at reasonable scale w/ Jacopo Tagliabue

Join us for the next text-based Ask-Me-Anything session with 

 to chat about shipping ML models to production faster. 

Bo is a Co-founder, and Sean is Head of Engineering at BentoML. 

 is an open-source unified model serving framework. It aims to simplify model serving and accelerate deployment. 

Bo has been working on the user side of the ML field helping DS teams to bring models to production. He is also an entrepreneur and founder of several startups, including Vrtigo, a platform for VR content creators acquired by Samsung. 

Sean is an expert in Software Engineering and Service Infrastructure. He worked at Amazon and Microsoft and led the service infrastructure team for distributed services at LinkedIn.

They both know the pain of bringing ML models to production firsthand and now focus on enabling data teams to ship their models faster. 

While you can ask Bo and Sean ANYTHING, here are a few topics as an example:

You can post your questions in advance or ask them live

. Bear in mind that if we have a lot of questions, we’ll prioritize live questions, though. The author of the best question gets recognition from Evidently. 