Matt Ranney discusses the software components that come together to make a self-driving Uber drive itself, and how they test new software before it is deployed to the fleet.
David Andrzejewski discusses how time series datasets can be combined with ML techniques in order to aid in the understanding of system behaviors in order to improve performance and uptime.
Rachel Thomas keynotes on three case studies, attempting to diagnose bias, identify some sources, and discusses what it takes to avoid it.
Holden Karau discusses how to train models, and how to serve them, including basic validation techniques, A/B tests, and the importance of keeping models up-to-date.
Michael Manapat discusses how Stripe evaluates and trains their machine learning models to fight fraud.
Adrian Cockcroft takes a look at best practices and challenges in getting to a chaos architecture mindset.
Ben Kehoe discusses the benefits of how the Internet of Things is right when you're producing connected devices, and that Serverless and Iot are a natural fit.
Dana Engebretson covers the contextual pros and cons of a number of architectural patterns given real world scalability constraints.
Guy Podjarny breaks into a vulnerable serverless application and exploits multiple weaknesses, helping better understand some of the mistakes people make, their implications, and how to avoid them.
Randy Shoup discusses managing data in microservices and shares proven patterns and practical advice that has been successful at Google, eBay, and Stitch Fix.
Jared Short dives into why, how, and when to pair Serverless & GraphQL, with takeaways for implementing the first greenfield Serverless GraphQL API or migrating existing APIs.
C. Richardson, R. Shoup, L. Ryan, R. Tangirala, and R. Schloming participate in a discussion on microservices and the challenges faced at scale, the strategies to use and more.