Control-M + Azure Machine Learning

Your Azure Machine Learning workflow starts before an endpoint pipeline ever executes — and it can fail when something upstream breaks. An Azure Blob Storage input didn’t arrive. An Azure Data Factory pipeline ran long. Control-M orchestrates the full chain: Azure Blob data arrival, Azure Data Factory transformation, Azure Machine Learning execution, and downstream delivery.

See it on a pipeline like yours View integration facts

common workflow issues

Does this sound like your week?

These aren’t edge cases. They’re the normal operating conditions for teams running Azure Machine Learning jobs across multiple tools. Here’s how Control-M handles each one.

UPSTREAM DATA

Your training run is due. Azure Blob data still hasn’t arrived.

Control-M keeps the Azure Machine Learning job dependent on the upstream workflow instead of a standalone time schedule. Execution starts only after required processing completes, keeping late data from becoming an incorrectly timed machine learning run.

ADF · AZURE ML

Your Data Factory pipeline finished. The machine learning job is still waiting.

Control-M coordinates Azure Data Factory and Azure Machine Learning jobs within the same scheduling environment, using job dependencies to release execution after upstream processing completes — eliminating disconnected schedules and manual handoffs between pipeline stages.

FAILURE RECOVERY

The pipeline job failed overnight. Everything downstream is still dependent on it.

Control-M tracks Azure Machine Learning job status and applies workflow conditions around the result, preventing dependent processing from continuing after a failed execution. Teams can recover the failed stage without blindly advancing the downstream workflow.

COMPUTE MANAGEMENT

Training is finished. The Azure ML compute cluster is still running.

Control-M can coordinate Azure Machine Learning compute management with the production workflow, including start, stop, restart, and delete actions. Compute lifecycle becomes another orchestrated step rather than a disconnected operational task after model processing finishes.

SLA RISK

The ML job is running long. Your morning delivery window is closing.

Control-M brings the Azure Machine Learning job into the end-to-end SLA workflow, so teams can see its impact on dependent processing and delivery deadlines instead of monitoring the ML execution independently from the business service.

Control‑M + Azure Machine Learning

workload.types	endpoint pipeline execution · existing job execution · compute management · compute start · compute stop · compute restart · compute delete
trigger.type	time schedule · upstream job completion · Control-M dependencies · file arrival workflow · API-driven execution · Automation API
cross_tool.deps	Azure Data Factory pipeline · Azure Blob Storage arrival · Azure Databricks job · Apache Airflow DAG · REST API call · downstream analytics
cloud.platforms	Microsoft Azure · Azure Machine Learning workspace · Azure compute · Control-M SaaS · hybrid Control-M environment
error_handling	failure tolerance · status polling frequency · Control-M job conditions · downstream cascade prevention · workflow recovery · SLA monitoring
throughput	asynchronous ML execution · long-running pipelines · batch inference workflows · compute-cluster processing
observability	job status · job results · job output · dependency visibility · SLA tracking · centralized workflow monitoring

end-to-end orchestration

One production workflow. Every tool in the stack.

Control-M orchestrates workflows across Azure Machine Learning, Azure Data Factory, Azure Blob Storage, Azure Databricks, Airflow, file transfers, and cloud services in a single job flow — with dependency tracking, SLA visibility, and automated recovery across all of them.

Cross-tool dependency: Azure Data Factory → Azure Machine Learning pipeline → Azure Databricks → analytics handoff
Data-aware triggers: file arrival, API event, upstream job completion, pipeline completion

View all integrations

Azure Machine Learning	pipeline execution · existing-job execution · compute management · status monitoring
Azure Data Factory	pipeline execution · completion dependencies · workflow handoff
Azure Blob Storage	file arrival · data dependencies · downstream release
Azure Databricks	job execution · dependency coordination · downstream processing
Apache Airflow	DAG execution · status tracking · workflow dependencies
File transfers	data movement · arrival detection · workflow release
Analytics services	downstream handoff · scheduled delivery · dependency coordination

airflow coexistance

Control-M doesn’t replace your Airflow DAGs.  It runs the layer above them.

The objection is common: “We’re already on Airflow.” The issue isn’t what Airflow does – it’s what happens before and after Airflow runs. That’s where pipelines actually fail.

Airflow manages its DAG. Control-M manages everything surrounding it.

AIRFLOW HANDLES

DAG-level orchestration inside the data pipeline

DAG-level task orchestration within data pipelines
Python operators, sensors, and task dependencies
Execution graph for jobs that run inside your pipeline
Manages retries within a single DAG context

control-m adds

The coordination layer around your DAGs

Coordination layer around DAGs — triggers Airflow based on upstream conditions: file arrivals, API events, other tool completions
Tracks each DAG’s SLA contribution across the full end-to-end workflow, not just its own routine
Manages failure recovery when upstream dependencies fail before Airflow even starts
Existing DAGs don’t need to be rewritten or migrated

MONITOR ML JOBS

Monitor Azure Machine Learning jobs across the pipeline

Azure Machine Learning shows execution within its own environment. Control-M connects that execution to the wider production pipeline, giving DataOps teams a centralized view of ML jobs alongside the upstream and downstream workloads that determine successful delivery:

Azure ML job status
Job results and output
Upstream and downstream dependencies
End-to-end workflow visibility
Cross-platform execution status

SLA ASSURANCE

Keep machine learning pipelines aligned to delivery SLAs

Azure Machine Learning can tell you how an ML job is executing. Control-M shows how that job affects the deadline for the complete production workflow, connecting ML execution with upstream dependencies, downstream processing, and service-level delivery:

End-to-end SLA monitoring
Cross-platform dependency visibility
ML job status monitoring
Downstream delivery coordination
Centralized workflow control

Documentation

Integrating Azure Machine Learning and Control-M

Bring order to complex workflows

Learn how Control-M helps teams orchestrate complex processes with greater visibility, coordination, and control.

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