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What We Got Wrong: Lessons from the Birth of Microservices
Ben Sigelman talks about what Google got wrong about microservices, the lessons learned along the way and how to apply those lessons today.
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From Winning the Microservice War to Keeping the Peace
Andrew McVeigh explains how to avoid common pitfalls when working with microservices.
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If You Don’t Know Where You’re Going, It Doesn’t Matter How Fast You Get There
Jez Humble and Nicole Forsgren explain the importance of knowing how (and what) to measure in order to focus on what’s important and communicate progress to peers, leaders, and stakeholders.
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Rethinking HCI with Neural Interfaces @CTRLlabsCo
Adam Berenzweig talks about brain-computer interfaces, neuromuscular interfaces, and other biosensing techniques that can eliminate the need for physical controllers.
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Using Data Effectively: beyond Art and Science
Hilary Parker talks about approaches and techniques to collect the most useful data, analyze it in a scientific way, and use it most effectively to drive actions and decisions.
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Building the Enchanted Land
Grady Booch examines what AI is and what it is not, as well as how it came to be and where it's headed. Along the way, he examines some best practices for engineering AI systems.
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Effective Java, Third Edition - Keepin' it Effective
Joshua Bloch covers some highlights from the third edition of “Effective Java”, concentrating on streams and lambdas.
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Inside a Self-driving Uber
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.
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Understanding Software System Behavior with ML and Time Series Data
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.
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Analyzing & Preventing Unconscious Bias in Machine Learning
Rachel Thomas keynotes on three case studies, attempting to diagnose bias, identify some sources, and discusses what it takes to avoid it.
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End-to-End ML without a Data Scientist
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.
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Counterfactual Evaluation of Machine Learning Models
Michael Manapat discusses how Stripe evaluates and trains their machine learning models to fight fraud.
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