Explore the potential of AI in addressing environmental challenges in this 54-minute podcast episode featuring Patrick Beukema, Head of Environmental AI at AI2. Delve into the intersection of remote sensing, high-performance AI, and global-scale real-time intelligence for sustainability and conservation efforts. Learn about the importance of MLOps in AI teams, the need for tight feedback loops in model improvement, and the integration of software engineering best practices in ML/AI workflows. Gain insights into environmental AI models, nature-inspired scientific advances, and iterative feedback-driven development. Discover the balance between various success metrics, model retraining pipelines, and the versatility of series models. Understand the benefits of edge models and custom models for specific data in environmental applications.
Overview
Syllabus
[] AI Quality Conference
[] Patrick's preferred coffee
[] Takeaways
[] Learning how to learn journey
[] Patrick's day to day
[] Environmental AI
[] Environmental AI models
[] Nature Inspires Scientific Advances
[] R&D
[] Iterative Feedback-Driven Development
[26:37 - ] LatticeFlow Ad
[] Balancing Metrics for Success
[] Model Retraining Pipeline
[] Series Models: Versatility
[] Edge Models Enhance Output
[] Custom Models for Specific Data
[] Wrap up
Taught by
MLOps.community
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