Which tool logs parameters, metrics and artifacts for each run in Azure Machine Learning?
Why D is the answer
MLflow
MLflow logs parameters, metrics and artifacts for each run in Azure Machine Learning.
In detail
- MLflow logs parameters, metrics and artifacts for each run.
- Autologging covers many frameworks.
Use case
An illustrative scenario, not a real incident.
A team logs parameters, metrics and the model file with MLflow, so each run can be compared and reproduced.
See it in different ways
flowchart LR A["Train"] --> B["Track with MLflow"] B --> C["Register a version"] C --> D["Deploy"] D --> E["Monitor"] E -->|"Threshold exceeded"| A
mindmap
root((Reproducible run))
Code
Git
Data
Versioned data assets
Environment
Registered environment
Compute
Cluster that scales to zero
Results
MLflowmindmap
root(("MLflow run"))
n1["Parameters"]
n2["Settings used"]
n3["Metrics"]
n4["Results"]
n5["Artifacts"]
n6["Model files"]Key point: MLflow logs runs in Azure Machine Learning.
Why the other options are wrong
- A. Azure Key Vault
- Key Vault stores secrets.
- B. GitHub Issues
- Issues track work.
- C. Bicep
- Bicep defines infrastructure.