ML System Design for Continuous Experimentation
In this webinar, we will examine some naïve ML workflows that don't take the development-production feedback loop into account and explore why they break down.
ML in the Field featuring Vignesh Shetty of GE Healthcare
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
Running Effective Machine Learning Teams
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.
Understanding the ML Model Lifecycle
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
Building Your Futureproof Stack for MLOps
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
MLOps System Design for Development and Production
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.
Overcoming Machine Learning Development Challenges
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.