Scaling responsible MLOps with Azure Machine Learning | BRK21

From the creator

When MLOps and responsible AI (RAI) are fully operationalized, organizations can build trusted ML solutions, increase the rate of experimentation, and accelerate time to market. With new capabilities integrated into Azure Machine Learning (AzureML), you’ll be able to apply RAI throughout the ML lifecycle using the new RAI dashboard and scorecard to improve fairness, explainability, and model performance. You’ll also learn how to simplify ML workflows with AzureML’s latest productivity capabilities. Additional Resource: Microsoft Learn for Azure -- https://aka.ms/AzureMSLearn?wt.mc_id=build22_breakout_webpage_esc Latest News: Gain agility with an integrated data platform -- https://aka.ms/BuildMay22/Datablog Recommended Next Step: Get more detailed content on this topic with a curated Session Resource Page -- https://aka.ms/Build22/Resource/AzureMachineLearning?wt.mc_id=build22_srp_breakout_azuremktg Microsoft Build 2022

Published 2022-05-27 · Watch on YouTube ↗
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