Rajat Monga talks about why Google built TensorFlow, an open source software library for numerical computation using data flow graphs, and what were some of the technical challenges in building it.
Matt Sakaguchi addresses the research and the insights of a manager who worked with his own team and others to instill the findings and principles from a pilot program at Google in the real world.
Rob Scherer and Rob Alford discuss the Design Sprint process used by Google Ventures, some of the changes made to it and lessons learned along the way.
Manuel Fahndrich describes how they tackled one particular resource allocation aspect of Google Cloud Dataflow pipelines - horizontal scaling of worker pools as a function of pipeline input rate.
Randy Shoup discusses modern service architectures at scale, using specific examples from both Google and eBay. He covers some interesting lessons learned in building and operating these sites.
Jessie Frazelle takes a look inside the tools built in Go centered around infrastructure and ops - from Docker to etcd to nsq and more.
John Wilkes shares lessons learned managing clusters at the scale of Google.
Randy Shoup tells war stories from Google and eBay focusing on how to scale code, infrastructure, performance, and operations, along with hard-won lessons learned in scaling them.
Tyler Akidau from Google demonstrates Google's Millwheel, a streaming system that promises low latency, strong consistency, and flexibility without relying on Lambda Architecture.
Melody Meckfessel explores how Google's engineering teams use CD to build products and scale them, and how their strain of DevOps speeds launches and helps their engineering culture thrive.
Raymond Blum discusses some of the challenges, solutions and discarded alternatives in creating durable storage systems at Google scale.
Gabbie Gibson introduces Google Glass, how to use voice commands, touch gestures and its interface, and how to write Glassware apps that run on the device.