Data Science @ IRIS.TV
by Dr. Thomas Sullivan, Chief Data Scientist and Joel Spitalnik, VP Engineering
While video recommendations best serve the viewer experience when presented in real time, machine learning is a computationally intensive process that is often done on the back end. IRIS.TV’s utilizes a hybrid process to synchronize computational tasks for learning so that engineering and data science teams can shrink the latency between analyzing history and presentation of recommended videos to improve the experience.
The LA Machine Learning Meetup began with events discussing a large variety of machine learning topics such as classification, clustering, neural networks, graphical algorithms, information retrieval, search, game theory, computational learning theory, reinforcement learning, collaborative filtering etc. More recently, a Data Science Track was launched with the focus on business applications and the entire process of data mining (e.g. business understanding, data collection, exploratory data analysis, data transformations, feature engineering, modeling, model validation, deployment, communication of results). While in fact Machine Learning is a part of Data Science and not the other way around, rather than starting a new meetup group, Data Science is featured within the already existing Machine Learning meetup.
