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Machine Learning

Past Presentations

Getting Started in Deep Learning with TensorFlow 2.0

The introduction of deep learning into the data science toolkit has allowed for significant improvements on many important problems in data science. Many advancements in fields such as natural language processing, computer vision and generative modeling can be attributed to advancements in deep...

Brad Miro Machine Learning Engineer @Google
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
From Research to Production With PyTorch

PyTorch is a powerful, flexible deep learning platform that enables engineers and researchers to move quickly from research to production. Since the 1.0 release a few months ago, researchers and engineers are already seeing success in taking advantage of the new capabilities to take deep learning...

Jeff Smith Engineering Manager @Facebook AI
Solving Payment Fraud and User Security with ML

Coinbase is the one of the largest digital currency exchanges in the world. We store about $1B of digital currency (bitcoin, litecoin, ether) on behalf of our users. Given the instant nature of digital currency and that it can't be reversed, we have one of the hardest payment fraud and security...

Soups Ranjan Director of Data Science @Coinbase
Time Predictions in Uber Eats

Uber Eats has been one of the fastest-growing meal delivery services since its initial launch in Toronto in December 2015. Currently, it’s available in over 40 countries and 400 cities. The ability to accurately predict delivery times is paramount to customer satisfaction and retention....

Zi Wang Leading the Machine Learning Engineering Work for Time Predictions @UberEats
Architecture & Algorithms Powering Search @ZocDoc

Most physician search systems require patients to know exactly what they’re looking for, either in terms of the appropriate specialty for a given condition or the medical terminology to describe the condition. At Zocdoc, we have built a patient friendly search system to power our core doctor...

Pedro Rubio Engineering Manager @Zocdoc
Brian D'Alessandro Director of Data Science @Zocdoc


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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