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Silicon Valley

Past Presentations

Modern Distributed Optimization

We often want to find the best settings for our systems, whether it’s configuring the best JVM parameters, optimizing user workflows, or selecting the right configuration for a machine learning algorithm. Black-box optimization techniques that can find good (hopefully optimal!) parameters have...

Matt Adereth Managing Director @TwoSigma
Scalable Chatbot Architecture with eBay ShopBot

ebay ShopBot is a personal shopping assistant available as a Facebook Messenger bot. It surfaces the best shopping options with the least effort in a conversational style. ShopBot leverages an array of AI components and the richness of ebay user data. In this session you will learn of the...

Robert Enyedi MTS Software Engineer @eBay
Modeling the Real World With Elixir/OTP

Building software that interacts with the real world is not as trivial as it sounds. When you build software that interacts with the real world, you have model your program to represent the real world. The traditional approach to modeling this is to model real-world events sequentially, one after...

Aish Dahal Engineer @pagerduty
Alibaba Container Platform Infrastructure - a Kubernetes Approach

As one of the biggest data companies in the world, Alibaba provides thousands of on-line/off-line services to various customers to support their business. Most of the Alibaba applications are fully containerized and run on top of Alibaba container platform which manages huge number of...

Fei Guo Senior Staff Engineer in Alibaba Container Platform Group
Managing Data in Microservices

This session is about the hard stuff -- managing data in microservices -- and about sharing proven patterns that have been successful at Google, eBay, and Stitch Fix. It begins with a quick tour of some prerequisites for being successful with microservices -- an organization of small teams with...

Randy Shoup VP Engineering @WeWork
Machine Learning from Theory to Practice

With recent advances in computational power, machine learning is positioned to change the way we interact with the world around us. Likewise, a surge of well-maintained machine learning libraries has made it possible for engineers to use machine learning models with minimal background. However,...

Deborah Hanus PhD candidate at Harvard University

Interviews

Haley Tucker Senior Software Engineer, Chaos Engineering @Netflix

UNBREAKABLE: Learning to Bend but Not Break at Netflix

Tell me about your talk.

I’m going to share my personal journey at Netflix learning to build and operate distributed systems -- both as a service owner and as a Chaos engineer.  As service owner, I’ll provide examples of how I used Chaos engineering to build better systems, even for non-critical services. As a chaos engineer, I’ll cover some of the...

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Amy Yin Software Engineer @Coinbase

Coinbase Commerce: A User-Controlled Payment Processor

QCon: Do I need to know anything about crypto or blockchain to attend this talk?

Amy: Absolutely not! Amy will explain private and public keys as well as blockchain addresses, which is all that is needed to understand the talk.

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Raghav Ramesh Real-Time Predictions @DoorDash

Engineering Systems for Real-Time Predictions @DoorDash

QCon: Can you describe the machine learning platform you have leverage at DoorDash?

Raghav: We built our system around common machine learning open source libraries in Python like SciKit-Learn, LightGBM, and Keras. We have a microservices architecture also built in Python which includes a prediction service that handles all the predictions and a features service. All the services are hosted on AWS.

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Aish Dahal Engineer @pagerduty

Modeling the Real World With Elixir/OTP

What is the focus of your work today?

I work on event-based systems that leverage Elixir/OTP and quite a bit of Apache Kafka. My team is building a platform for enriching and processing high volumes of data in real time.

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Mike Lee Williams Research engineer @Cloudera Fast Forward Labs

Probabilistic Programming from Scratch

What do you want someone to leave your talk with? 

The audience will leave with a strong non-mathematical intuition for how Bayesian inference allows us to quantify the strength of conclusions drawn from real-world data. They’ll hopefully be excited to solve other toy problems with the tool we put together during the talk, and keen to check out PyMC3.

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Seth Katz Senior Software Engineer, Operational Insights @Netflix

How Machines Help Humans Root Cause Issues @Netflix

You're at Netflix. What team are you working on, and what's the focus of the work that you do?

I work on a team called operational insights.

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