{"categories":[{"label":"Distributed Computing","url":"https://skillfed.io/packages/category/system-distributed-computing/2"}],"enrichment":{"capability":"Integrates MLflow experiment tracking and model management with Dagster's data orchestration, allowing you to track ML model training and artifacts as part of your Dagster asset pipelines.","skillfed_tags":["ml-orchestration","experiment-tracking","data-pipeline"],"use_cases":["Log model training metrics and parameters to MLflow while orchestrating the training job as a Dagster asset.","Track model artifacts and versions in MLflow as outputs of Dagster asset runs.","Build reproducible ML pipelines where experiment tracking is integrated into the asset lineage and observability.","Manage model promotion and versioning through MLflow's registry while keeping orchestration logic in Dagster.","Monitor ML experiment runs alongside data pipeline execution in a unified Dagster web UI."],"what_it_does":"dagster-mlflow is a library that bridges MLflow's experiment tracking and model registry with Dagster's asset-oriented orchestration framework. It allows you to instrument Dagster assets with MLflow tracking decorators so that model training runs, metrics, parameters, and artifacts are automatically logged to MLflow while remaining part of your Dagster lineage graph. This is useful when you want to track ML experiments and models alongside your data pipelines, keeping both the orchestration and the ML metadata in one place.\n\nThe package depends on dagster, mlflow, pandas, and protobuf. It is maintained as part of the main Dagster project and receives regular updates. The integration is designed to work within Dagster's declarative asset model, so you define your ML workflows as Dagster assets and use the integration to connect them to MLflow's tracking backend.","worth_installing":"Yes. The package is actively maintained, has no known vulnerabilities, and low install friction. It is worth installing if you are already using both Dagster and MLflow and want to unify experiment tracking with your data orchestration. If you use only one of these tools, it adds no value."},"id":"dagster-mlflow","links":{"html":"https://skillfed.io/packages/dagster-mlflow","md":"https://skillfed.io/packages/dagster-mlflow.md","pypi":"https://pypi.org/project/dagster-mlflow/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-14","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"dagster-mlflow","python_support":"supports_current","summary":"Package for mlflow Dagster framework components."},"popularity":{"monthly_downloads":144479,"position":11151,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.29.18"}