Increase Data Science efficiency security and privacy using MLOps in Azure Machine | INT135C

From the creator

MLOps (of DevOps for Machine Learning) is all about accelerating the machine learning lifecycle in a secure and trusted way. Get to production faster, with optimized experiences for reproducible model training, packaging, validation, deployment and monitoring. See in action how to use MLOps to store, version and track ML assets and collect continuous feedback on model behavior. Understand how to leverage multi-factor authentication, role-based authorization, data encryption, VNETs, and other security and privacy best practices. Learn how to maintain quality models and deliver real business value and customer impact. Microsoft Learn -- https://docs.microsoft.com/en-us/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=Build2020_presentation_additionalresources Microsoft Docs -- https://docs.microsoft.com/en-us/azure/machine-learning/?WT.mc_id=Build2020_presentation_additionalresources Channel 9 -- https://channel9.msdn.com/Search?term=build2020%20azureml&lang-en=true Analyst Reports - Azure AI -- https://azure.microsoft.com/en-us/overview/ai-platform/analyst/?WT.mc_id=Build2020_presentation_additionalresources Customer Testimonial - Translink -- https://customers.microsoft.com/en-us/story/768972-translink-travel-and-transportation-azure?WT.mc_id=Build2020_presentation_additionalresources Microsoft Build 2020

Published 2020-05-26 · Watch on YouTube ↗
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