Unlike most analytics events, PAW is densely focused. Its agenda commits specifically to the commercial application of machine learning – also known as predictive analytics. The conference doesn't cover "data science" as a whole, which is a much broader and less well-defined area, that, for example, can include standard business intelligence reporting and such.

At PAW, you'll hear directly from the horse's mouth precisely how Fortune 500 analytics competitors and other companies of interest deploy machine learning and the kind of business results they achieve. More than most events, we pack the agenda as densely as possible with named case studies. 

PAW isn't run by an analytics vendor and the speakers aren't trying to sell you on anything but good ideas. PAW speakers understand that vendor-neutral means those in attendance must be able to implement the practices covered and benefit from the insights delivered without buying any particular analytics product.

To summarize, PAW events deliver brand-name, cross-industry, vendor-neutral case studies purely on machine learning deployment, and the hottest topics and techniques.