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Silicon Valley

Past Presentations

What Google Learned about Creating Effective Teams

Over the years, researchers have conducted numerous studies on team dynamics. At Google, our People Analytics sought to identify those attributes that set effective teams apart from ineffective ones. The driver was that while so much of work at Google is done on teams, Googlers are rated...

Matt Sakaguchi Site Reliability Manager @Google
Unifying Banks & Blockchains @Coinbase

With over 6 million users across 33 countries, Coinbase is an industry leader building the bridge between traditional finance and the blockchain space. Our goal at Coinbase is to provide simple and secure exchange services to users worldwide for blockchain-based digital assets. The networks that...

Jim Posen Tech Lead @Coinbase
Engineering Systems for Real-Time Predictions @DoorDash

Today, applying machine learning to drive business value in a company requires a lot more than figuring out the right algorithm to use; it requires tools and systems to manage the entire machine learning product lifecycle. For instance, we need systems to manage data pipelines, to monitor model...

Raghav Ramesh Real-Time Predictions @DoorDash
PracticalDDD: Bounded Contexts + Events => Microservices

Domain Driven Design and Messaging go hand in hand, like a warm chocolate brownie paired with vanilla ice-cream! DDD is a software discipline that allows you to move faster and write high-quality code. The whole point is to align the software you write to be flexible with the business changes....

Indu Alagarsamy Solution Architect @Particular Software
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

Interviews

Michele Titolo Tech Lead and Architect @Square

No Microservice Is an Island

You worked at Capital One first and then switched over to Square. So what were you working on at Capital One?

At Capital One, I was working on the first layer of services that our mobile app and website hit. Any new mobile or web request would first reach the service owned by my team.  This service implemented security and customisation logic and then made a server request to the broader Capital one ecosystem. Capital One has a lot of...

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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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