Databricks

For me, the main benefit is that it is little or no work to enable. For example, when autologging is enabled for a library like sklearn or Pytorch, a lot of information about a model is captured with no additional steps. Further in Databricks, the tr...

For the tracking server? Yes, it does produce logs which you could see if running the tracking server as a standalone service. They are not exposed from the hosted tracking server in Databricks. However there typically aren't errors or logs of intere...

You could monkey patch MLFlow's _get_dbutils() with something similar to this to get this working while connecting from dbconnectspark = SparkSession.builder.getOrCreate() # monkey-patch MLFlow's _get_dbutils() def _get_dbutils(): return DBUtils(...

import mlflow client = mlflow.tracking.MlflowClient() runs = client.search_runs("my_experiment_id", "", order_by=["metrics.rmse DESC"], max_results=1) best_run = runs[0]https://mlflow.org/docs/latest/python_api/mlflow.tracking.html#mlflow.tracking.M...

There shouldn't be. Generally speaking, models will be serialized according to their 'native' format for well-known libraries like Tensorflow, xgboost, sklearn, etc. Custom model will be saved with pickle. The files exist on distributed storage as ar...