Description
Haris Pozidis, PhD
Manager Cloud Storage & Analytics, Principal RSM, Master Inventor, IBM Research
Snap Machine Learning (Snap ML) is a new software library for training machine learning models that is characterized by very high performance, scalability to TB-scale datasets and high resource efficiency. It continuously evolves and currently supports generalized linear models, decision trees, random forests and gradient boosting machines. Snap ML has been built to address the needs of business applications, which often have to deal with data that is big in size, react fast to changing environments, and use resources efficiently to drive down cost. In addition, with the most recent addition of a new gradient boosting model, Snap ML offers high generalization accuracy, which drives higher profits in AI-infused business applications. In this talk I will present the principles of the Snap ML library, explain how it achieves high speed and scalability, and present several cases of business workloads that demonstrate the benefits reaped by Snap ML.
