Having collected Big Data, organizations are now keen on data science and "Big Learning."
Much of the focus has been on data science as exploratory analytics, offline, in the lab. However, building a production-ready large-scale operational analytics system remains a difficult and ad-hoc endeavor, especially when real-time answers are required. Design patterns for effective implementations are emerging, which take advantage of relaxed assumptions, adopt a new tiered "lambda" architecture, and pick the right scale-friendly algorithms to succeed.
Drawing on experience from customer problems, this session presents a reference architecture and algorithm design choices for a successful implementation.