Design Patterns for Large-Scale Real-Time Learning
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 will present a reference architecture and algorithm design choices for a successful implementation.
Sean is the Director of Data Science at Cloudera. He has over 13 years of experience in the data science field.
- big data