07.07 · Walkthrough
Orchestration for ML Training
Orchestrate a training-data generation, model training, evaluation, and model registration workflow with clear dependencies.
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ML training orchestration turns data preparation, training, evaluation and registration into a repeatable production workflow. Each stage has explicit inputs, outputs and success conditions, so jobs run in the right order, artefacts remain traceable, failures can be retried, and only validated models reach the registry.
What this lesson answers
- how to orchestrate an ML training pipeline
- how should model registration depend on evaluation
- how to make ML training runs reproducible
Notes
Orchestration for ML training is about turning a set of related scripts and jobs into a reliable, repeatable workflow with explicit dependencies. In a typical training pipeline, you may need to generate or extract training data, validate and transform it, train a model, evaluate the results, and register the model if it meets quality requirements. Each step depends on outputs or conditions from earlier steps, so the orchestrator’s job is to run them in the correct order, pass metadata and artifacts between them, retry transient failures, and make the state of the workflow visible.
For a…
Common questions
- What does orchestration mean for ML training?
- It means managing the training process as a dependency graph rather than a collection of ad hoc scripts. Data generation, validation, training, evaluation and registration are separate tasks with defined inputs and outputs. The orchestrator schedules them, carries metadata between them, handles retries and exposes workflow state.
- Why should training data be versioned in a pipeline?
- Versioned training data lets you reproduce a model run later. The training job should refer to a specific dataset or feature snapshot, not a moving source. That makes metrics, artefacts and configuration meaningful, and supports debugging, audit, rollback and comparison against earlier models.
- When should a trained model be registered?
- A model should be registered only after evaluation succeeds against the required criteria. The evaluation step should use the actual artefact produced by training and compare its metrics with thresholds or a baseline. Registration then publishes the approved model with metadata needed for deployment and rollback.
Short definition: what is Orchestration for ML Training?