MLOps for Fairness - Creating Comprehensive Fairness Workflows

Explore MLOps strategies for identifying, measuring, and remediating bias in machine learning systems at scale in this 20-minute conference talk by Bhaktipriya Radharapu, Tech Lead for Responsible AI at Google. Gain insights into the causes of algorithmic bias and learn about fairness metrics. Dive deep into bias remediation techniques across all stages of the ML lifecycle, including data collection, pre-processing, training, and post-processing. Discover a range of Python tools vetted by the academic ML community and proven effective for industry-level challenges. Learn how to create comprehensive fairness workflows and implement best practices to not only flag fairness issues but also address them in your ML projects.

MLOps for Fairness: Creating Comprehensive Fairness workflows