Difference between revisions of "MLflow"

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* [http://github.com/mlflow/mlflow MLflow | GitHub]
 
* [http://github.com/mlflow/mlflow MLflow | GitHub]
 
* [[Analytics]] ... [[Visualization]] ... [[Graphical Tools for Modeling AI Components|Graphical Tools]] ... [[Diagrams for Business Analysis|Diagrams]] & [[Generative AI for Business Analysis|Business Analysis]] ... [[Requirements Management|Requirements]] ... [[Loop]] ... [[Bayes]] ... [[Network Pattern]]
 
* [[Analytics]] ... [[Visualization]] ... [[Graphical Tools for Modeling AI Components|Graphical Tools]] ... [[Diagrams for Business Analysis|Diagrams]] & [[Generative AI for Business Analysis|Business Analysis]] ... [[Requirements Management|Requirements]] ... [[Loop]] ... [[Bayes]] ... [[Network Pattern]]
* [[Development]] ... [[Notebooks]] ... [[Development#AI Pair Programming Tools|AI Pair Programming]] ... [[Codeless Options, Code Generators, Drag n' Drop|Codeless, Generators, Drag n' Drop]] ... [[Algorithm Administration#AIOps/MLOps|AIOps/MLOps]] ... [[Platforms: AI/Machine Learning as a Service (AIaaS/MLaaS)|AIaaS/MLaaS]]
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* [[Development]] ... [[Notebooks]] ... [[Development#AI Pair Programming Tools|AI Pair Programming]] ... [[Codeless Options, Code Generators, Drag n' Drop|Codeless]] ... [[Hugging Face]] ... [[Algorithm Administration#AIOps/MLOps|AIOps/MLOps]] ... [[Platforms: AI/Machine Learning as a Service (AIaaS/MLaaS)|AIaaS/MLaaS]]
 
* [http://towardsdatascience.com/empowering-spark-with-mlflow-58e6eb5d85e8 Empowering Spark with MLflow | Albert Franzi - Towards Data Science]
 
* [http://towardsdatascience.com/empowering-spark-with-mlflow-58e6eb5d85e8 Empowering Spark with MLflow | Albert Franzi - Towards Data Science]
 
* [http://www.kdnuggets.com/2018/07/manage-machine-learning-lifecycle-mlflow.html Manage your Machine Learning Lifecycle with MLflow]
 
* [http://www.kdnuggets.com/2018/07/manage-machine-learning-lifecycle-mlflow.html Manage your Machine Learning Lifecycle with MLflow]

Latest revision as of 20:51, 26 April 2024

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MLflow is an open source platform to manage the ML lifecycle, including experimentation, reproducibility and deployment. It currently offers three components::

  1. Tracking — Record and query experiments: code, data, config, and results.
  2. Projects — Packaging format for reproducible runs on any platform.
  3. Models — General format for sending models to diverse deployment tools.


mlflowObjects.jpg