Patterns, Architecture, & Best Practices: Scaling Machine Learning Algorithms with Azure

In this talk, we will talk about the patterns, reference architecture, and best practices when scaling your machine learning algorithms in Azure. More specifically, we will talk about:_x000D_
i. Typical reference architecture and demo on using Azure DSVM to develop your small model, use Azure HDInsight to scale out the model, and use containers or VMs to operationalize your model_x000D_
ii. How to use different machine learning libraries in Azure HDInsight and blend them to analyze your data and train models, including SparkR, Microsoft R Server, and other third party libraries such as H2O._x000D_
iii. And we will also cover the team data science approach when talking about the above bullet points.