{
  "channels": [
    "https://repo.anaconda.com/pkgs/main"
  ],
  "conda_build_version": "3.26.0",
  "conda_version": "23.7.2",
  "description": "MLflow is a platform to streamline machine learning development, including tracking\nexperiments, packaging code into reproducible runs, and sharing and deploying models.\nMLflow offers a set of lightweight APIs that can be used with any existing machine\nlearning application or library (TensorFlow, PyTorch, XGBoost, etc), wherever you\ncurrently run ML code (e.g. in notebooks, standalone applications or the cloud).\n",
  "dev_url": "https://github.com/mlflow/mlflow",
  "doc_url": "https://mlflow.org",
  "env_vars": {
    "CIO_TEST": "<not set>"
  },
  "extra": {
    "copy_test_source_files": true,
    "feedstock-name": "mlflow",
    "final": true,
    "flow_run_id": "471af7c7-1741-43ec-b56e-7a443caf6e00",
    "recipe-maintainers": [
      "harupy",
      "aarondav",
      "ahirreddy",
      "andrewmchen",
      "aveshcsingh",
      "dbczumar",
      "jaroslawk",
      "mateiz",
      "mparkhe",
      "pogil",
      "smurching",
      "sueann",
      "tomasatdatabricks",
      "xhochy",
      "zangr",
      "janjagusch"
    ],
    "remote_url": "git@github.com:AnacondaRecipes/mlflow-feedstock.git",
    "sha": "d83069ec91313b71082b61bf50df9e51296d70d9"
  },
  "home": "https://mlflow.org/",
  "identifiers": [],
  "keywords": [],
  "license": "Apache-2.0",
  "license_family": "APACHE",
  "license_file": "LICENSE.txt",
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  "summary": "MLflow: A Machine Learning Lifecycle Platform",
  "tags": []
}