| Data storage | Azure Blob Storage / Data Lake Gen2 | - Data Lake Gen2 adds folders and fast analytics on big datasets
- Connects to Azure ML datastores, Synapse and Databricks
- Hot, cool and archive tiers to balance speed and cost
|
| Notebooks | Azure ML compute instance notebooks | - Managed notebooks that also open in VS Code
- Attach datastores and compute in a few clicks
- Auto-shutdown schedules stop forgotten machines
|
| GPU training | Azure ML compute clusters on NC-series (T4 / A100) GPUs | - NC / ND GPUs from T4 up to A100 and H100
- Low-priority VMs reduce training costs
- Compute clusters scale to zero when idle
|
| ML platform & registry | Azure Machine Learning (MLflow-based registry) | - MLflow-native tracking and registry, portable to other platforms
- Responsible AI dashboard for fairness and explanations
- Designer and AutoML for low-code model building
|
| Serverless serving | Azure Functions (container) or Azure Container Apps (scale to zero) | - Container Apps scale to zero between requests
- Pay per request or per second of use
- Trigger predictions from queues or new files with Functions
|
| GPU serving | Azure ML managed online endpoint on GPU SKUs | - Managed online endpoints with blue/green deployments
- Autoscaling and built-in monitoring
- Secure with keys or Microsoft Entra ID
|
| Batch predictions | Azure ML batch endpoints, scheduled by Azure ML schedules | - Process large datasets in parallel on a cluster
- Clusters shut down when the job finishes
- Results written to Blob Storage
|
| Pipelines | Azure ML pipelines | - Reusable components shared across projects
- Schedules and triggers for automatic retraining
- Lineage between data, runs and registered models
|
| Vector database | Azure AI Search or Azure Database for PostgreSQL + pgvector | - Azure AI Search combines keyword, vector and semantic ranking
- Plugs straight into Azure AI Foundry for RAG
- PostgreSQL + pgvector for relational and vector data together
|
| LLM access | Azure AI Foundry (Claude, OpenAI and others) | - Claude, OpenAI and open models under Azure governance
- Content safety filters built in
- Private networking and regional deployments
|
| Monitoring | Azure Monitor / Application Insights + Azure ML model monitoring | - Application Insights traces each request end to end
- Azure ML watches for data drift and prediction quality
- Alerts can go to email or Teams
|
| Privacy controls | Private endpoints, customer-managed keys, regional data residency | - Private endpoints keep traffic on Microsoft's network
- Customer-managed encryption keys
- Fine-grained access with Microsoft Entra ID
|