Track: Applied Machine Learning
Day of week:
The world is becoming more intelligent every day. A growing number of appliances and applications are collecting information and sending it back to the mothership to be analyzed, dissected, and fed into model generators (a.k.a. machine learning algorithms). In turn, these model generators send these trained models back to these applications or appliances to modify their behavior. This is the basis of any device with “Smart” in its name. It is also the basis of any web or mobile services that recommend goods and services to you. From smart homes to smart cars, personalized buying to job recommendations, systems that understand speech and video to those that prevent fraud, these systems benefit from applied machine learning. Come to this track to learn about the technologies and practices that power these use-cases.
by Leah McGuire
PhD-Level Data Scientist @Salesforce focused on using data to build products
80-90% of data science is data cleaning and feature engineering. However, if we were to plot a count of what all the data science tools are for, we would find that most innovation happens in data infrastructure and modeling. We want to change that and make data scientists much more productive while also improving the quality of their work.
In this talk I will describe the machine learning platform we wrote on top of spark to modularize these steps. This allows easy reuse of components...
by Lucian Vlad Lita
Director of Data Engineering @Intuit
In the early days of personalization, the focus was on smarter, more complex machine learning models, on algorithms and optimizations. Later, the attention shifted to feature engineering as a driver for accuracy. Finally, the community focused on data as the next frontier: volume, quality, cleansing, and clean labeling. In this talk, we focus on the crucial next step in personalization: well designed software architectures for storing, computing, and delivering responsive, accurate in-...
by Dmitry Chechik
Discovery Team Engineer @Pinterest
The Pinterest Homefeed personalizes and ranks 1B+ pins for 100M+ users on Pinterest, using data gathered from collaborative filtering, user curation, web crawl, and many more. This talk will give an overview of the system and focus on effective engineering choices made to enable productive ML development. To have multiple engineers effectively develop, test, and deploy machine-learned models for the Pinterest Homefeed, we’ve built a system that allows for continuous training and feature...
by Oscar Boykin
Data Scientist @Twitter
Today, tooling for ad-hoc data science is fairly well understood. But when you want to create a repeated process such as analytics or prediction systems, things tend to change with time, and how to deal with such change is not always clear. Columns and features are added and removed. New models are developed. Data errors are discovered and corrected. How can we build a data pipeline system to handle these demands? This talk will discuss some of the systems challenges and solutions that arise...
by Brian Wilt
Director, Head of Data Science and Engineering @Jawbone
In Jurassic Park, scientists mined dino DNA from mosquitoes trapped in fossilized amber. But before they could use that DNA to clone a dinosaur, there was a catch: the sequences were damaged and incomplete. Ultimately, they needed frog DNA to infer the missing gaps to get a complete DNA sequence to clone dinosaurs.
The Jawbone UP system captures health data through a battery of sensors on your wrist and app on your phone -- your movement, sleep, and heart rate. But this data is often...
Covering innovative topics
Monday Nov 16
Architectures You've Always Wondered About
Silicon Valley to Beijing: Exploring some of the world's most intrigiuing architectures
Applied Machine Learning
How to start using machine learning and data science in your environment today. Latest and greatest best practices.
Browser as a platform (Realizing HTML5)
Exciting new standards like Service Workers, Push Notifications, and WebRTC are making the browser a formidable platform.
Modern Languages in Practice
The rise of 21st century languages: Go, Rust, Swift
Our most innovative companies reimagining the org structure
Level up your approach to problem solving and leave everything better than you found it.
Tuesday Nov 17
Containers in Practice
Build resilient, reactive systems one service at a time.
Architecting for Failure
Your system will fail. Take control before it takes you with it.
Modern CS in the Real World
Real-world Industry adoption of modern CS ideas
The Amazing Potential of .NET Open Source
From language design in the open to Rx.NET, there is amazing potential in an Open Source .NET
Keeping life in balance is always a challenge. Learning lifehacks
Unlearning Performance Myths
Lessons on the reality of performance, scale, and security
Wednesday Nov 18
Streaming Data @ Scale
Real-time insights at Cloud Scale & the technologies that make them happen!
Taking Java to the Next Level
Modern, lean Java. Focuses on topics that push Java beyond how you currently think about it.
The Dark Side of Security
Lessons from your enemies
Taming Distributed Architecture
Reactive architectures, CAP, CRDTs, consensus systems in practice
Lessons on building highly effective organizations