This is a 3-part series that will explore different ways to develop and train your model in Microsoft Azure.
In our first session, we will deploy a data science virtual machine and train a model via the Machine Learning Server available on that platform. We will cover tasks associated with a machine learning project including project scoping, business and data understanding, procuring and preparing data, training and evaluating one or more models, tuning models for better performance, and then deploying the chosen model if it meets or exceeds the defined level of accuracy. Each of these tasks is complex in and of itself, and each requires both technical skills and compute resources to drive success. The compute resources needed for training and evaluating multiple models can be significant, and thus lend themselves to the use of on demand and distributed resources available in the cloud.
In the second session we will explore how to set up an Azure Machine Learning workspace and connect to it programmatically via a notebook environment. Finally, we will look at how we can train a model graphically via the Azure ML designer.
About The Presenter
RJ Daskevich, DCM is a senior consultant and training instructor for Stone Door Group’s AI and Machine Learning practice. He is both a Hadoop Certified Developer and Google Certified Data Engineer.
About Stone Door Group
Stone Door Group helps customers transition to an AI and ML, enabling digital enterprise in a consumable way. To speak with RJ and our team of experts, send us an email at letsdothis@stonedoorgroup.com or visit our website today.
