Speaking at O'REilly AI Conference

​Executive Briefing: A new taxonomy of machine learning

Session details 

Rachel Silver shares a new taxonomy of machine learning approaches that distinguishes between those that are providing enormous competitive advantage and those that represent merely small, incremental improvements on existing analytical tools and details a framework for evaluating ML approaches on several dimensions of complexity, including:

The amount of data required (such as for training)
The computational complexity of the training algorithm
Real-time streaming requirements (versus just batch computing)
Data throughput for the deployed model to process
Rachel explores examples of how to apply this framework to real-world machine learning approaches and highlights the technical requirements of supporting the most disruptive examples of ML solutions.