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Four real-life Machine Learning use cases

Этот ресурс доступен на English.

Опубликовано: 22.06.2020

This guide walks you through four practical end-to-end machine-learning use cases on Azure Databricks: 

• A loan risk analysis use case that covers importing and exploring data in Databricks, executing ETL and the ML pipeline, including model tuning with XGBoost Logistic Regression 

• An advertising analytics and click prediction use case that includes collecting and exploring the advertising logs with Spark SQL and using PySpark for feature engineering and using GBTClassifier for model training and predicting the clicks 

• A market basket analysis problem at scale, from ETL to data exploration using Spark SQL, and model training using FT-growth 

• An example of suspicious behavior identification in videos, including a preprocessing step for creating image frames, transfer learning for featurization, and applying logistic regression to identify suspicious images in a video.