Azure Machine Learning

Azure ML's Automated ML feature automatically iterates through different algorithms, feature engineering steps, and hyperparameter combinations to find the best-performing model for a given dataset. It supports classification, regression, and time-series forecasting tasks, and produces a ranked leaderboard of candidate models with metrics. Once the best model is identified, you can register it directly to the Azure ML model registry for deployment.

Yes, Azure ML is framework-agnostic and supports PyTorch, TensorFlow, scikit-learn, XGBoost, and virtually any Python-based ML library through its curated environments and custom Docker-based compute environments. You can train models on Azure's managed GPU compute clusters and use distributed training frameworks like DeepSpeed or Horovod for large-scale workloads. Azure ML also has deep integration with ONNX Runtime for deploying trained models across platforms.

An Azure ML workspace is the top-level resource container that holds all your assets—datasets, experiments, compute clusters, registered models, deployed endpoints, and environment definitions. Every job you submit, every model you train, and every endpoint you deploy is associated with a workspace, which also provides role-based access control and activity logging. You typically create one workspace per team or project, and it is provisioned as an Azure resource within a resource group.

Yes, Azure ML supports deployment to Azure Kubernetes Service (AKS) for real-time online endpoints, to Azure Container Instances for dev-and-test scenarios, and to Kubernetes clusters attached to the workspace for managed online endpoints. For edge deployment, Azure ML integrates with Azure IoT Edge, allowing you to package models as Docker containers that run on edge hardware. Models can also be exported to ONNX format for inference on devices that cannot maintain a cloud connection.