Create a new ML workspace
Step‑by‑step guide to set up an Azure Machine Learning workspace using the portal, CLI, and SDK.
Deploy a model as a web service
Learn how to package, register, and deploy a trained model using Azure Container Instances or AKS.
Configure automated ML experiments
Automate model selection and hyperparameter tuning with Azure Automated ML.
Set up a CI/CD pipeline for ML models
Integrate Azure Machine Learning with Azure DevOps for continuous integration and delivery.
Monitor model performance in production
Implement data drift detection, logging, and alerting for deployed models.
Use feature store for data management
Best practices for storing, versioning, and retrieving features across projects.
Run distributed training with PyTorch
Configure multi‑node GPU clusters for large‑scale PyTorch training.
Secure access with managed identities
Enable Azure Managed Identities to protect credentials in ML pipelines.