Practical Automated Machine Learning on Azure

Index

A

• accuracy, Choosing Evaluation Metrics
• aggregations, Generate features
• algorithm selection, Algorithm Selection
• Apache Spark, Azure Databricks and Apache Spark
• area under curve (AUC), Choosing Evaluation Metrics
• artificial intelligence, The Machine Learning Process
• auto-featurization
  • for automated machine learning, Auto-Featurization for Automated ML
  • for classification and regression, Auto-Featurization for Classification and Regression-Auto-Featurization for Classification and Regression
  • for time-series forecasting, Auto-Featurization for Time-Series Forecasting-Auto-Featurization for Time-Series Forecasting
  • selecting type of, Auto-Featurization for Automated ML
• automated machine learning
  • on Azure (see Azure Machine Learning service)
  • benefits of, Bringing It All Together
  • best practices, Best Practices for Machine Learning Projects-Don’t Operate in a Silo
  • code-free machine learning, Automated ML for Everyone-Power BI Automated ML to Azure Machine Learning
  • development environments, Automated ML for Developers-Conclusion
  • feature engineering in (see feature engineering)
  • for classification and regression, Using Automated ML for Classification and Regression-Selecting and testing the best model from the experiment run
  • growing demand for, Growing Demand
  • how it works, How Automated ML Works-End-to-End Model Life-Cycle Management
  • iterative process of, An Iterative and Time-Consuming Process-The End-to-End Process
  • model deployment, Deploying Automated Machine Learning Models-Conclusion
  • model interpretability ...