Scaling your AI/ML practices with MLOps and Azure Machine Learning

From the creator

On this week's AI Show, Abe Omorogbe, Setu Chokshi and Scott Donohoo stop by to talk about how to utilize AzureML MLOps capabilities to streamline the process of moving ML experiments from training to inference. They'll demo how AzureML can be used to securely train, deploy and manage ML models in production environments and highlight practices the AzureML team has used to helped customers standardize on AzureML to scale their ML Ops practices. Chapters: 00:00 Livestream begins 03:31 Welcome to the AI Show 05:00 On today's show 11:02 Scaling your AI/ML practices with MLOps and Azure Machine Learning 12:34 Part 1 - What's new in AI/ML 13:40 What is MLOps 16:47 Enterprise Machine Learning Lifecycle 20:45 Learn more: https://aka.ms/MLOps/Learn 21:27 Q&A 24:32 Part 2 - Azure MLOps (v2) Solution Accelerator 26:21 The Value Proposition 29:00 Customer use cases 30:35 Solution Accelerator goals 32:50 Q&A 36:27 Part 3 - Solution Accelerator 38:25 Demo 49:32 Learn more https://github.com/Azure/mlops-v2 50:50 Q&A 1:03:14 Wrap Resources: Learning Modules to upskill your MLOps practices https://aka.ms/MLOps/Learn MLOps Maturity Model to evaluate MLOps adoption and take an incremental approach to MLOps adoption https://aka.ms/MLOps/MaturityModel MLOps Solution Accelerator to bootstrap MLOps environments https://github.com/Azure/mlops-v2 Setup MLOps in Microsoft Docs https://aka.ms/MLOps/Setup Connect: Seth | https://twitter.com/sethjuarez Cassie | https://twitter.com/Cassieview Watch

Published 2023-05-09 · Watch on YouTube ↗
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