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Machine Learning

Past Presentations

How Machines Help Humans Root Cause Issues @Netflix

Automated root cause detection represents a holy grail goal for many systems monitoring tools. It’s also an extremely challenging domain. A successful approach must generalize well from limited examples, handle highly dimensional data, understand the application domain, and perform well in a...

Seth Katz Senior Software Engineer, Operational Insights @Netflix
ML Data Pipelines for Real-Time Fraud Prevention @PayPal

PayPal processes about a billion dollars of payment volume daily ($451bn in FY2017); complex decisions are made for each transaction or user action, to manage risk and compliance, while also ensuring good user experience. PayPal users can make payments immediately in 200 regions with the...

Mikhail Kourjanski Lead Data Architect, Risk and Compliance Management Platform @PayPal
Software Is Eating the World, ML Is Going to Eat Software

"Democratizing ML" is a hot topic these days - particularly in industry. Efficiency, composability and accessibility of machine learning technology are active areas of investment for many research and product groups. Unfortunately, while machine learning has the potential to fundamentally improve...

Joe Pamer Language Designer Working on ML + Tooling @Facebook & previously Developed TypeScript, F#, & Swift
Machine Learning Open Space

Open Space is a simple way to run productive meetings from 5 to 2000 or more people, and a powerful way to lead any kind of organization in everyday practice or extraordinary change. In Open Space sessions, participants create and manage their own agenda of parallel...

Hands-On Feature Engineering for Natural Language Processing

Think of Grammarly, Autotext and Alexa, as many applications in software engineering are full of natural language, the opportunities are endless. The latest advances in NLP such as Word2vec, GloVe, ELMo and BERT are easily accessible through open source Python libraries. There is no better time...

Susan Li Sr Data Scientist at Kognitiv Corporation
Panel: ML for Developers/SWEs

Throughout the day, we'll have speakers cover how they've adopted applied machine learning to software engineering. The day wraps with a discussion from the speakers on taking an applied, pragmatic approach to adding ML to your systems and how they solved challenges. Eager to deploy ML...

Hien Luu Engineering Leader @LinkedIn - AI & Big Data Enthusiast
Jeff Smith Engineering Manager @Facebook AI
Brad Miro Machine Learning Engineer @Google
Ashi Krishnan Building the Next Generation of Developer Tools @Github

Interviews

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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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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Golestan Sally Radwan PhD in AI and Computational Biology

From Software Development to ML - A Team's Transformation

QCon: What is the focus of your work?

Sally: Almost a year ago, I quit my job to finish my Phd. in AI and BioInformatics. It involves the use of AI techniques in the research of genetics and computational biology. Based on my Phd. research, I am also starting a company in the same space.

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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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Corey Zumar Software Engineer @databricks

MLflow: An Open Platform to Simplify the Machine Learning Lifecycle

QCon What is the focus of your work today?

The focus of my work is MLflow: an open source platform for the complete machine learning lifecycle. The MLflow platform provides solutions for data collection, data preparation, model training, and model productionization.

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Jeff Smith Engineering Manager @Facebook AI

From Research to Production With PyTorch

What is the focus of your work today?

I work on PyTorch, which is an open source deep Learning framework developed here at Facebook. I specifically work with the team on a lot of the ways in which we enable the success of a broad open source community that spans both bleeding edge researchers as well as ML product engineers putting deep learning technology to use in...

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