ABSTRACT
Determining meaningful answers from large, complex datasets is a difficult and lengthy problem. Even with the support of validated machine learning algorithms, there is still a lot of coding obstacles to understand before using them.
In this session, you will learn about the functionality of Azure Machine Learning’s Automated ML service, which not only helps you build machine learning models efficiently using one algorithm, but also runs through and compares dozens of different algorithm setups, scores them, and helps you gain insight from multiple perspectives. All whilst offering low-code/no-code options to get started
It allows data scientists, analysts, and developers to build ML models with high scale, efficiency, and productivity all while sustaining model quality.
AGENDA
- Introduction to Automated Machine Learning
- Introduce Azure Automated Machine Learning no-code experience with Demo Example
- Options for deployment and inference in Azure Machine Learning
- Bonus - Options for retraining using MLOps