SQLBits 2023
Automated Machine Learning on Azure
AutoML which stands for Automated Machine Learning empowers data teams to quickly build and deploy machine learning models. It aims to reduce the time and expertise required to generate a machine learning model by automating the heavy lifting of preprocessing, feature engineering, model creation, tuning and evaluation.
When it comes to machine learning in Microsoft Azure, there are two main options for running your AutoML: (1) Azure Machine Learning Service and (2) Azure Databricks. This session will aim to introduce how to develop ML models using AutoML on both platforms, as well as the features of each and why you would choose one over the other.
AutoML which stands for Automated Machine Learning empowers data teams to quickly build and deploy machine learning models. It aims to reduce the time and expertise required to generate a machine learning model by automating the heavy lifting of preprocessing, feature engineering, model creation, tuning and evaluation.
When it comes to machine learning in Microsoft Azure, there are two main options for running your AutoML: (1) Azure Machine Learning Service and (2) Azure Databricks.
This session will aim to introduce how to develop ML models using AutoML on both platforms, as well as the features of each and why you would choose one over the other.
When it comes to machine learning in Microsoft Azure, there are two main options for running your AutoML: (1) Azure Machine Learning Service and (2) Azure Databricks.
This session will aim to introduce how to develop ML models using AutoML on both platforms, as well as the features of each and why you would choose one over the other.