Training and deploying with your own code on MLflow
MLflow is an open source platform for machine learning (https://mlflow.org). It was initiated by Databricks (https://databricks.com), who also brought us Spark. MLflow has lots of features, including the ability to deploy Python-trained models on SageMaker.
This section is not intended to be an MLflow tutorial. You can find documentation and examples at https://www.mlflow.org/docs/latest/index.html.
Installing MLflow
On our local machine, let's set up a virtual environment for MLflow and install the required libraries. The following example was tested with MLflow 1.17:
- We first initialize a new virtual environment named
mlflow-example. Then, we activate it:$ virtualenv mlflow-example $ source mlflow-example/bin/activate
- We install MLflow and the libraries required by our training script:
$ pip install mlflow gunicorn pandas sklearn xgboost boto3
- Finally, we download the Direct Marketing dataset we already...
