Comet Resources

GE Healthcare projects are delivering REAL impactful business contributions, including reducing MRI imaging time by up to 50% while improving image quality, 30-50% reduction in exam time and 70% reduction in no-show rates. Listen to this in-depth interview and learn: -How large of an AI/ML team is needed for these impactful projects -What level of industry/domain expertise is needed by AI practitioners

While academic research has been improving consistently, many organizations are struggling with translating ML into business value. Now is the time to strategize with your team to overcome critical operational hurdles of ML teams.

What is the machine learning lifecycle? Watch this webinar to learn: The stages of the ML model lifecycle Why it’s critical that machine learning teams track their models through the entire lifecycle

In this webinar, join the teams at Pachyderm and Comet as we cover: What MLOps entails, and the components of a robust stack The challenges teams face when scaling their models intro production

In our discussions with leading organizations utilizing ML like The RealReal and Uber, we have compiled real-world case studies and organizational best practices for MLOps in the enterprise.

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.

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.

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.

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.