Azure ML
ML lifecycle management on Azure
0 case studies
4 specialists
General Infrastructure ML lifecycle management on Azure
Running ML experiments, managing model versions, and deploying models as endpoints within Microsoft's Azure cloud. It integrates tightly with Azure DevOps for MLOps pipelines and is the primary path for enterprises already in the Microsoft ecosystem to operationalize AI.
Create an Azure Machine Learning workspace from the Azure Portal or via the Azure CLI. Install the azure-ai-ml Python SDK and authenticate using az login. You'll need an Azure subscription, a resource group, and a compute target (cluster or instance) for training jobs.
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AI Leadership Institute
CEO of the AI Leadership Institute and 4x Microsoft MVP. Expert in ethical AI, enterprise workflows, and responsible innovation. Helps organizations navigate AI transformation with a focus on governance and responsible deployment.
Harvey AI
Co-founder and CTO of Harvey AI. AI researcher who previously worked on large language models at Google Brain and Meta. Harvey has achieved 94.8% accuracy on document Q&A and exceeded lawyer performance in four benchmark tasks. Under his technical leadership, Harvey serves 235 enterprise customers including the world's top law firms.
Intelligent World
CEO of Intelligent World and one of the most prominent AI influencers globally. Expert in enterprise AI agents, logistics, manufacturing, and infrastructure applications. Advises organizations on AI strategy and digital transformation.
Microsoft Research / AI Now Institute
Principal Researcher at Microsoft Research and co-founder of the AI Now Institute at NYU. Author of 'Atlas of AI,' one of the most influential books on the social and political implications of artificial intelligence. Research professor at USC Annenberg. Leading voice on AI accountability and governance.
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