Evolving Metrics
Daniel and Sara discuss the shift from relying on top line metrics to considering disaggregated metrics in the ML community. They explore the importance of understanding algorithmic bias and the need for benchmarks that align with the desired outcomes of the systems being developed.In this clip
From this podcast
The Gradient
Sara Hooker: Cohere For AI, the Hardware Lottery, and DL Tradeoffs
Related Questions
What metrics are important in evaluating artificial intelligence in the context of the episode "Analyzing the Google Paper on Continuous Delivery in ML // Part 4 // MLOps Coffee Sessions #17" and the clip "Model Validation Challenges"?
What metrics are important in evaluating artificial intelligence in the context of the episode Analyzing the Google Paper on Continuous Delivery in ML // Part 4 // MLOps Coffee Sessions #17 and the clip Model Validation Challenges?
How should product managers think about metrics in relation to the episode Jennifer Prendki Interview - Agile Machine Learning - TWiML Talk #46, considering the insights from the episode Peter Skomoroch — Product Management for AI and the clip Constructing Good Benchmarks?