Mark Price talks about techniques for making performance testing a first-class citizen in a Continuous Delivery pipeline.
Matt Long talks about some approaches to environment infrastructure testing that his team at OpenCredo has created using Ruby.
Tom Johnson and Gel Goldsby talk about scaling problems they encountered at Unruly, and where extreme programming values led them.
Scott Le Grand describes his work at NVidia, Amazon and Teza, including the DSSTNE distributed deep learning framework.
Shirshanka Das describes LinkedIn’s Big Data Infrastructure and its evolution through the years, including details on the motivation and architecture of Gobblin, Pinot and WhereHows.
Neha Batra presents her experience with pair programming at Pivotal Labs. They pair program eight hours/day every workday and help enable other companies to practice it with them.
Danny Yuan discusses how Uber builds its next generation of stream processing system to support real-time analytics as well as complex event processing.
Dan McKinley discusses how Etsy is using data to validate their ideas and prototypes, turning some into real products.
Margarette Purvis shares Food Bank’s kaizen journey of rethinking and improving operations by implementing small incremental improvements across the organization.
Sayli Karmarkar discusses Winston, a monitoring and remediation platform built for Netflix engineers.
Lisa Phillips discusses the typical struggles a company runs into when building around-the-clock incident operations and the things Fastly has put in place to make dealing with incidents easier.
Stefan Krawczyk discusses how StitchFix used the cloud to enable over 80 data scientists to be productive and have easy access, covering prototyping, algorithms used, keeping schema in sync, etc.