Scott Clark introduces Bayesian Global Optimization as an efficient way to optimize ML model parameters, explaining the underlying techniques and comparing it to other standard methods.
Juan Batiz-Benet makes a short intro of IPFS (the InterPlanetary File System) and discusses the IPLD data model and example data structures (unixfs, keychain, post).
Mark Derricutt discusses the importance of having different read and write data models when working with RESTful web APIs.
Adam Ernst shows how his team at Facebook encountered spiraling complexities and declining reliability and decided to make the shift to functional, in the data model and the view layer of News Feed.
Sponsored by Basho. Sean Cribbs discusses the theory behind several rich data types introduced with Riak 2.0 and then walking through some example applications that use them in popular languages.
Garrett Eardley explores how Riot Games is using Riak for their stats system, discussing why they chose Riak, the data model and indexes, and strategies for working with eventually consistent data.
Robin Johnson discusses using a data management model for games that can be scaled, and the bottlenecks and challenges met by OMGPOP scaling to millions of users.
Claudia Perlich keynotes on M6D’s approach to Big Data, using data granularity to build predictive models used for user targeting, bid optimization and fraud detection.
Tony Tam shares tips for modeling data with MongoDB for a fast and scalable system based on his experience migrating billions of records from MySQL to MongoDB.
Kenneth M. Anderson shares some of the data modeling issues encountered while transitioning from a relational database to NoSQL.
Terry Bunio discusses applying Agile principles to building a data warehouse based on a data model and making use of visualization tools.
Peter Bell presents several patterns for modeling and retrieving data from graph databases using Neo4j in his examples.