[{"data":1,"prerenderedAt":12},["ShallowReactive",2],{"stack-tr-en-azure-machine-learning":3},{"tagline":4,"description":5,"strengths":6,"useCase":11},"Azure's managed ML platform, with native MLflow tracking and Entra ID integration.","Azure Machine Learning organizes work in workspaces: notebooks and managed compute, AutoML and a drag-and-drop designer, jobs and pipelines defined in YAML or the Python SDK, MLflow-native tracking and model registry, then managed online and batch endpoints with blue-green deployment. It relies on Entra ID for identities and roles, on Azure DevOps or GitHub Actions for MLOps, and on Azure OpenAI and the Azure AI Foundry catalog for generative AI. It's our choice when the client's IT is standardized on Microsoft.",[7,8,9,10],"MLflow-native tracking and registry: existing MLflow code works as is.","AutoML and visual designer to prototype, SDK and YAML to industrialize.","Managed online and batch endpoints, blue-green deployment and traffic splitting.","Entra ID, RBAC, private networking and Azure DevOps: MLOps that fits corporate IT.","ML inside a Microsoft IT estate, governed MLOps, forecasting and scoring, predictive maintenance.",1792675980160]