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GLOSSARY
iso_doc_type: "Description" iso_viewpoint: "ContextView" type: "architecture" title: "Ubiquitous Language & Domain Glossary" description: "Comprehensive domain definitions and ubiquitous acronyms for the MLOps Python Package." tags: ["iso42010", "glossary", "ddd", "mlops"] timestamp: "2026-10-01T17:50:00Z" generated: "agent:wiki-iso-documentation-standard" verified: "true" last_verified_commit: "073b5fd"
Purpose: Establish an unambiguous vocabulary across data science, MLOps engineering, platform operations, and compliance auditors.
A reference machine learning model (BaselineSklearnModel) providing an empirical baseline for regression tasks. It implements the unified Model interface using Scikit-Learn pipelines and Random Forest estimators.
A model governance pattern executed by PromotionJob. A newly trained and evaluated model (Challenger) is benchmarked against the currently active production model (Champion). Only if performance exceeds predefined thresholds is the Challenger promoted to the production alias.
An open-source version control system for machine learning projects, managing data pipelines and large dataset pointers in Git without tracking large binary blobs.
Feature importance and local sample attributions computed by ExplanationsJob using the SHAP (SHapley Additive exPlanations) TreeExplainer algorithm, enforcing strict schema compliance on feature rankings.
A strict governance constraint requiring human sign-off for critical operational transitions, particularly for merging pull requests into default branches and deploying models to live customer-facing endpoints.
A cryptographic attestation utility (utils/signers.py) calculating a SHA-256 hash across serialized prediction inputs and outputs to ensure data provenance and tamper-evident auditing.
A standardized Python context manager interface (__enter__, __exit__, run) that coordinates service start/stop, alert notifications, and error logging across all operational pipelines.
Metadata linking generated datasets and models back to the originating input data sources, feature engineering steps, commit SHAs, and runtime environment parameters.
A data validation framework enforcing static and runtime tabular constraints on Pandas DataFrames (InputsSchema, TargetsSchema, OutputsSchema), guaranteeing schema safety at service boundaries.
A security mechanism embedded in controller/kafka_app.py utilizing a sliding window algorithm to throttle excess incoming prediction requests and prevent algorithmic Denial of Service (DoS).
A mathematical strategy abstraction (utils/splitters.py) partitioning datasets into train and test subsets while respecting temporal sequence constraints (TimeSeriesSplitter) or random stratification (TrainTestSplitter).
Related: Master Index · Strategic Design