dash-mlops 0.1.4

ML lifecycle management for Databricks — preprocessing, evaluation, drift monitoring, governance, and champion/challenger promotion, with a built-in notebook UI

DashML — Databricks Library

Part of the Dashlibs suite — Databricks libraries built for business users.

ML lifecycle management: preprocessing, drift monitoring, evaluation (SHAP + model cards), governance artifacts, and champion/challenger promotion — driven from one notebook UI, backed by Unity Catalog and MLflow.

Installation

%pip install dash-mlops

Quick Start

import dashml
dashml.launch()   # Opens interactive UI in your Databricks notebook

What it covers

Area	Entry points
Preprocessing	clean_dataframe(), dashml.transforms (outlier removal, binning, lag features, ...)
Drift monitoring	ModelMonitor (PSI + chi-squared, optional auto-retrain trigger)
Evaluation	explain_features() (SHAP), build_model_card(), check_thresholds()
Governance	build_governance_artifacts() (signature, features, fairness, approval record)
Registry	RunTracker, register_model(), promote_challenger() (UC @champion alias)
Experimentation	dashml.experiment — compare/promote MLflow runs
Serving	dashml.serving.sync_serving_endpoint()

Everything beyond ModelMonitor and the notebook UI is also directly importable for use in a training script — launch() is the guided path, not the only path.

Part of Dashlibs

Library	Purpose
dash-dq	Data Quality
dash-synthetic	Synthetic Data Generation
dash-ml	ML Lifecycle Management
dash-ingest	Data Ingestion
dash-gov	Data Governance
dash-ontology	Ontology & Lineage for AI

License

Apache 2.0