On Demand Archives

Visualize your Object Detection Models with Jacques Verré

October 27, 2022
Claire Pena

Whether you’re comparing model performance during a daily standup or onboarding a new teammate, you’ll need to log the training runs with an experiment management tool like Comet. In this session, Jacques Verré will walk you through the process of reviewing a YOLOv5 model in Comet.

Dark blue banner that reads: "less friction, more ML"

2021 ML Practitioner Survey

August 8, 2022
Sharmila Chockalingam

AI is encountering another hurdle to delivering value, in the form of friction among and between teams. A survey of 508 machine learning practitioners that included data scientists and engineers found challenges related to people, process, and tools. This friction can cause delays in ML development that delay or halt model deployment to production.

Graphic announcing Convergence, and upcoming ML industry conference. Set against a blue and purple planetary backdrop.

Convergence 2022 – ML Highlights from 2021 and Lessons for 2022

June 30, 2022
Sharmila Chockalingam

Oren Etzioni, CEO at Allen Institute for Artificial Intelligence, was the keynote speaker at Comet's Convergence 2022 event, where he summarizes 15 highlights of 2021 in ML and suggests lessons for 2022 and beyond.

MLOps System Design for Development and Production

June 30, 2022
Sharmila Chockalingam

Comet CEO Gideon Mendels discusses system design principles for managing development-production feedback loops and shares industry case studies these principles are applied to production ML systems.

More than a Statistic: Diversity in Data Science and Machine Learning

June 30, 2022
Sharmila Chockalingam

Diversity has become an HR catchphrase, but what does it really mean to be from an underrepresented group in tech? This webinar explores the ongoing discussions about diversity, equity, and inclusion in ML and data science.

Overcoming Machine Learning Development Challenges

June 30, 2022
Sharmila Chockalingam

In this webinar, Gideon Mendels shares the results of Comet’s 2021 ML Practitioner Survey and talks to Ancestry's Stanley Fujimoto about overcoming ML development challenges.

Reproducibility in ML Development

June 29, 2022
Sharmila Chockalingam

Reproducibility can be a barrier to ensuring positive outcomes and scaling great work. Learn about four aspects of reproducibility in ML and a five-point checklist for ensuring ML reproducibility across your organization.

Run open source LLM evaluations with Opik!

Visualize your Object Detection Models with Jacques Verré

2021 ML Practitioner Survey

Convergence 2022 – ML Highlights from 2021 and Lessons for 2022

MLOps System Design for Development and Production

More than a Statistic: Diversity in Data Science and Machine Learning

Overcoming Machine Learning Development Challenges

Reproducibility in ML Development

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