Machine Learning Pipelines with Azure ML in 2026: Step-by-Step

Build and deploy ML pipelines on Azure ML: components, compute, and endpoints.

## Machine Learning Pipelines with Azure ML in 2026 Azure Machine Learning is Microsoft's managed platform for the whole ML lifecycle. Here is how teams build pipelines on it in 2026. ### Core concepts - **Workspace**: the top-level resource holding everything - **Compute**: managed clusters (CPU/GPU) that run jobs - **Components**: reusable, versioned pipeline steps (data prep, train, eval) - **Pipeline**: DAG of components - **Endpoint**: online (real-time) or batch scoring ### A minimal pipeline in Python SDK v2 [python] from azure.ai.ml import MLClient, command client = MLClient.from_config() pipeline = command( code="./", command="python train.py --data ${{inputs.data}}", inputs={"data": "azureml:my_dataset:1"}, environment="azureml:AzureML-pytorch-2.4", compute="gpu-cluster", display_name="train-model" ) client.jobs.create_or_update(pipeline) [/python] ### The 2026 workflow 1. Create workspace and compute (a 24 GB GPU node for fine-tuning) 2. Register datasets as versioned assets 3. Build 3 components: preprocess, train, evaluate 4. Run the pipeline, track metrics in the UI 5. Register the best model, deploy to an online endpoint with autoscaling 6. Monitor with data drift detection built into Azure ML ### Cost notes - Compute stops when jobs finish (pay per job, not per month) - Spot VMs cut GPU costs 60-80% for retraining - Endpoint minimum: 1 node always-on for online serving ### FAQ **Azure ML vs plain Kubernetes?** Azure ML removes cluster management; you pay a platform premium. **Can I use my own models?** Yes - bring any model, including local LLMs via custom environments.