Setting Up an Azure Machine Learning Development Environment
Setting Up an Azure Machine Learning Development Environment starts with an overview of machine learning. In particular, its relationship with generative AI and a machine learning model’s lifecycle. Then, the lesson moves on to the computing requirements, methods, and tools for model development and training, including script and asset management.
Learning Objectives
- Overview of machine learning
- Model training compute requirements
- Overview of model deployment
- Overview of model development tools and methods
Intended Audience
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Students preparing for the DP-100: Designing and Implementing a Data Science Solution on Azure exam and those wanting a practical guide on setting up a development environment for model development
Prerequisites
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There are no machine learning prerequisites, but some familiarity with Azure is assumed
Hallam is a software architect with over 20 years experience across a wide range of industries. He began his software career as a Delphi/Interbase disciple but changed his allegiance to Microsoft with its deep and broad ecosystem. While Hallam has designed and crafted custom software utilizing web, mobile and desktop technologies, good quality reliable data is the key to a successful solution. The challenge of quickly turning data into useful information for digestion by humans and machines has led Hallam to specialize in database design and process automation. Showing customers how leverage new technology to change and improve their business processes is one of the key drivers keeping Hallam coming back to the keyboard.