Exporting and loading pipelines with MLeap
When we train a machine learning or deep learning model, our intention is to be able to use it several times to predict new observations of data. To do this, we must be able to not only store the model but also load it back again into one or more platforms. Therefore, we encounter the need to serialize the model for future use in scoring or predictions.
MLeap is a commonly used format to serialize and execute machine learning and deep learning pipelines made in popular frameworks such as Apache Spark, scikit-learn, and TensorFlow. It is commonly used for making individual predictions rather than batch predictions. These serialized pipelines are called bundles and can be exported as models and later be loaded and deployed back into Azure Databricks to make new predictions.
In this section, we will learn how to use MLeap to export and load back again a DecisionTreeClassifier MLlib model to make predictions using a saved pipeline in...
