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Data-driven systems and machine learning continue to be a significant trend across our industry. However, most attempts at these systems face serious difficulties due the tension between the clean, controlled, lab environments where statisticians apply their skills, and the messy unpredictable, production environments where we want to apply their results at scale.
In this talk, we will provide an overview of the machine learning landscape, with an emphasis on the distinction between machine learning as a scientific practice and the larger concept of machine learning systems. Using this base, we will walk through the challenges of taking machine learning out of the lab and applying it successfully in an industrial setting.
By the conclusion of this talk, the audience should take away a better understanding of machine learning as a practice, together with an idea of what it takes to build and deploy machine-learning systems in an environment that deals with real customers and data at scale.
Machine Learning, Data, Scale, Deployment
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