mlflow.tensorflow
MLflow integration for TensorFlow.
Manages logging and loading TensorFlow models as Python Functions. You are expected to save your own
saved_models
and pass their paths to log_saved_model()
so that MLflow can track the models.
In order to load the model to predict on it again, you can call
model = mlflow.pyfunc.load_pyfunc(saved_model_dir)
,
followed by prediction = model.predict(pandas DataFrame)
to obtain a prediction in a
pandas DataFrame.
The loaded PyFunc model does not expose any APIs for model training.

mlflow.tensorflow.
load_pyfunc
(saved_model_dir) Load model stored in Python Function format. The loaded model object exposes a
predict(pandas DataFrame)
method that returns a Pandas DataFrame containing the model’s inference output on an input DataFrame.Parameters: saved_model_dir – Directory where the model is saved. Return type: Pyfunc format model with function model.predict(pandas DataFrame) > pandas DataFrame)
.

mlflow.tensorflow.
log_saved_model
(saved_model_dir, signature_def_key, artifact_path) Log a TensorFlow model as an MLflow artifact for the current run.
Parameters:  saved_model_dir – Directory where the exported TensorFlow model is saved.
 signature_def_key –
The signature definition to use when loading the model again. See `SignatureDefs in SavedModel for TensorFlow Serving
<https://www.tensorflow.org/serving/signature_defs>`_ for details.  artifact_path – Path (within the artifact directory for the current run) to which artifacts of the model will be saved.