Brian Wilt discusses how applied machine learning techniques and data science helped Jawbone build a successful fitness tracking app.
Lucian Vlad Lita focuses on the next step in personalization: well-designed software architectures for storing, computing, and delivering responsive, accurate in-product predictions and experiments.
Leah McGuire describes the machine learning platform Salesforce wrote on top of Spark to modularize data cleaning and feature engineering.
Matt Ranney covers the evolution of Uber's architecture and some of the systems they built to handle the current scaling challenges.
Based on his experience at Uber, Matt Ranney explores why the build or buy tradeoff is so difficult, and makes some recommendations for both vendors and users.
Bill Buxton argues that we need to rethink how we design software and how we think about applications to prevent our entire industry from stalling.
Norman Maurer presents how Apple uses Netty for its Java based services and the challenges of doing so, including how they enhanced performance by participating in the Netty open source community.
Josh Evans uses the Netflix Operations Engineering team as a case study to explore the challenges faced by centralized engineering teams and approaches to addressing those challenges.
Olaf Carlson-Wee explores key strategies to keep a company safe from a wide range of malicious actors in the virtual Wild West.
Tyler McMullen discusses how probabilistic algorithms actually work in practice and how to know they'll be safe and reliable in critical production systems.
Bridget Kromhout discusses how to work with the right level of abstraction with DevOps tooling, how different DevOps pieces fit together into a cohesive solution.
Nitesh Kant describes how embracing asynchrony in Netflix applications, from networking to business processing, creates gracefully degrading and highly resilient applications.