agent_specifications

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iso_doc_type: "Specification" iso_viewpoint: "ComponentView" type: "architecture" title: "Lifecycle Job Specifications & Contracts" description: "ISO 42010 ComponentView / ISO 15289 Specification documentation detailing the functional contracts of all 6 MLOps lifecycle jobs." tags: ["iso42010", "okf", "component_view", "specification", "jobs"] timestamp: "2026-10-01T17:50:00Z" generated: "agent:wiki-iso-documentation-standard" verified: "true" last_verified_commit: "073b5fd"

Lifecycle Job Specifications & Contracts

Purpose: Formal specification of the inputs, outputs, execution logic, and error behaviors for all 6 operational jobs in jobs/.

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1. Unified Job Matrix

Job Name	Module Source	Primary Responsibility	Key Inputs	Primary Outputs
TrainingJob	jobs/training.py	Model training & artifact logging	Raw Parquet datasets, model config	Trained model, MLflow run, metrics
TuningJob	jobs/tuning.py	Hyperparameter optimization	Search space grid, metric objective	Best parameters, trial metrics
EvaluationsJob	jobs/evaluations.py	Validation against threshold gates	Test inputs, targets, model alias	Evaluation report, threshold pass/fail
ExplanationsJob	jobs/explanations.py	SHAP interpretability extraction	Sample inputs, registered model	Global importance & sample SHAP values
InferenceJob	jobs/inference.py	Batch prediction & cryptographic signing	Unlabeled inputs, model version	Output predictions, SHA-256 signature
PromotionJob	jobs/promotion.py	Champion-challenger alias promotion	Candidate model version, target alias	Updated MLflow model alias tag

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2. Detailed Job Contracts

TrainingJob (jobs/training.py)

• Inputs: inputs (ParquetReader), targets (ParquetReader), model (ModelKind), run_config (RunConfig).
• Execution Flow:
  1. Reads inputs and targets DataFrames and validates against Pandera schemas.
  2. Partitions data using the configured Splitter.
  3. Fits the underlying Model on train splits.
  4. Computes train and test evaluation metrics.
  5. Logs parameters, metrics, and serialized model artifact to active MLflow run.
• Exceptions: pandera.errors.SchemaError, FileNotFoundError.

TuningJob (jobs/tuning.py)

• Inputs: inputs, targets, model, searcher (OptunaSearcher / GridCVSearcher), metric.
• Execution Flow:
  1. Orchestrates cross-validated hyperparameter trials over parameter grids.
  2. Evaluates scoring metrics on validation folds.
  3. Records trial histories and identifies optimal hyperparameter set.
  4. Logs best parameters and trials dataframe to MLflow.

EvaluationsJob (jobs/evaluations.py)

• Inputs: inputs, targets, model_type, run_config.
• Execution Flow:
  1. Ingests evaluation test splits.
  2. Generates predictions using designated model artifact.
  3. Evaluates all configured metrics (RegressionMetricsEnum).
  4. Compares results against predefined Threshold constraints.

ExplanationsJob (jobs/explanations.py)

• Inputs: inputs_samples, models_explanations, samples_explanations, alias_or_version.
• Execution Flow:
  1. Instantiates shap.TreeExplainer over model tree estimators.
  2. Calculates global mean absolute SHAP feature importances.
  3. Computes local SHAP attribution values per input sample.
  4. Writes outputs to Parquet datasets matching SHAPValuesSchema and FeatureImportancesSchema.

InferenceJob (jobs/inference.py)

• Inputs: inputs (ReaderKind), outputs (WriterKind), loader (LoaderKind), alias_or_version.
• Execution Flow:
  1. Loads model artifact via Loader adapter.
  2. Validates incoming tabular input against InputsSchema.
  3. Generates prediction outputs matching OutputsSchema.
  4. Signs input-output bundle using InferSigner and writes result to destination storage.

PromotionJob (jobs/promotion.py)

• Inputs: alias (str), version (int | None).
• Execution Flow:
  1. Queries MLflow Model Registry for the specified registered model.
  2. Validates that candidate model passed all evaluation criteria.
  3. Re-assigns designated model alias (e.g. champion) to target version.

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Related: Runtime Sequences · Tactical Design · Master Index