Pilosa, as a technology, changes the dialog around large data sets, both static and in motion. Historically data lakes like Hadoop have been used to store massive amounts of data. However, it is estimated that only 20% of that data is practically analyzable because complex analytical operations on an ad-hoc basis become computationally painful and slow. Next DSS MIA Event - https://datascience.salon/miami/ Next DSS AUS Event - https://datascience.salon/austin/ Enter a distributed binary index: Pilosa. While this can be used to unlock and join massive datasets and streams, it can also be thought of as an accelerator for training Machine Learning models and most importantly running your algorithms in large scale production environments. In this workshop Hypergiant will discuss how Pilosa interacts with several ML ideas including the Winnow algorithm, association schemes, and recommendation engines.