Until recently, if you wanted to build machine learning (ML) models, you needed a strong background in data science, or the ability to hack your way through more advanced parts of the ML/Artificial Intelligence (AI) workflow. Understanding ML frameworks, the differences between algorithms within them, and optimization of their so-called “hyperparameters,” has been tough – at times even impenetrable. Enter automated machine learning (AutoML) technology, which automates algorithm selection and hyperparameter tuning and, in the case of Microsoft’s AutoML, does so in a fashion friendly to .NET developers and other Microsoft technology pros.
In this Fast Focus presentation, we’ll cover the basics of AutoML (and the algorithm/hyperparameter concepts underlying it), then summarize the vendors and offerings in the broader AutoML market. Next, we’ll drill down on Microsoft AutoML and how it surfaces in .NET, Visual Studio, Azure Machine Learning and even Power BI.
You will learn:
- What machine learning algorithms, hyperparameters and models are
- How Microsoft’s AutoML, and other AutoML platforms, automate the AI drudge work
- How to integrate AI into your skillset without needing to acquire a deep data science background