Deploy and Serve Model from Azure Databricks onto Azure Machine Learning
Description
We demonstrate how to deploy a PySpark based Multi-class classification model trained on Azure Databricks using Azure Machine Learning (AML) onto Azure Kubernetes (AKS) and associate the model to web services. This presentation covers end-to-end development cycle; from training the model to using it in web application. Machine Learning problem formulation The current solutions to detect semantic types of tabular data mostly rely on dictionary/vocabulary, regular expressions and rule-based look up to identify the semantic types. However, these solutions are 1. Not robust to dirty and complex data and 2. Not generalized to diverse data types. We formulate this into a Machine Learning problem by training a multi-class classifier to automatically predict the semantic type for tabular data. Model Training on Azure Databricks We choose Azure Databricks to perform the featurization and model training using PySpark SQL and Machine Learning Library. To speed up the featurization process, we leverage the PySpark Functions (UDF) to register and distribute the featurization functions into UDFs. For the model training, we pick the Random Forests as the classification algorithms and optimiz…
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