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Model Training Pipeline

Complete supervised learning model training pipeline from data ingestion to model artifact storage with experiment tracking and hyperparameter optimization.

Complexity: Complex | Duration: 30m+ | Category: Devops

Tags: ml training supervised-learning mlops

Workflow Diagram​

Steps​

Step 1: Data validation​

Agent: data

scientist - Verify training data quality, schema, and distributions

Step 2: Feature engineering​

Agent: ml

engineer - Create/transform features, handle missing values, encode categoricals

Step 3: Train/test split​

Agent: ml

engineer - Stratified split with reproducible random seed

Step 4: Model training​

Agent: ml

engineer - Train model with hyperparameter optimization (grid/random/bayesian)

Step 5: Experiment tracking​

Agent: ml

engineer - Log metrics, parameters, and artifacts to MLflow/Weights&Biases

Step 6: Model validation​

Agent: testing

specialist - Validate on holdout set, check for overfitting

Step 7: Model artifact save​

Agent: ml

engineer - Serialize model and metadata to versioned storage

Step 8: Quality review​

Agent: testing

specialist - Final accuracy/F1/AUC review against acceptance criteria

Usage​

To execute this workflow:

/workflow devops/model-training-pipeline.workflow

See other workflows in this category for related automation patterns.