Track: Commoditized Machine Learning


Day of week:

Trends in the commoditization and increased adoption of machine learning across the engineering world. This includes the new abstractions that are allowing teams to build upon increasingly complex and capable ML systems.

Track Host:
Pete Soderling
Founder, Hakka Labs
Pete Soderling is the founder of Hakka Labs, an international community of open-source software engineers. He is a self-taught engineer who has developed online platforms & applications since the very early days of the internet. Since 2004, Pete has been a serial tech entrepreneur in NYC and the San Francisco Bay Area and has built engineering teams for each business he has started, including Stratus Security. Stratus built a turn-key, cloud-based solution that helps data providers easily distribute data to their partners, also handling data metering, monitoring and security. Before Stratus, Pete founded and was the CEO of mechanikal, an industry-leading software development agency that specialized in the development of secure Internet applications for mobile devices, desktops and the web. Over the course of the last five years Pete has worked as an engineering ambassador for companies like Amazon, Tumblr, IHeartRadio, eBay, and NBC Universal helping them build their technical teams and attract top engineering talent. Pete holds his B.S. in Information Systems Management from New York University where he graduated with honors. Pete's current venture is Hakka Labs, where he is building a platform that creates original content, resources and training events to help software engineers level up their skills. With 20 years experience in technology, Pete epitomizes his belief in Marc Andreessen's quote that "software is eating the world," as he continuously dedicates himself to his passion.
10:35am - 11:25am

by David Beyer
Investor at Amplify Partners, Co-founder @, Founding Team @ Patients Know Best

by Rob Witoff
Director @Coinbase

An Overview of ML Adoption Across Industry - David Beyer

Society is facing a profound transformation in the nature of work, the role of data and the future of the world's major industries. Intelligent machines will play a variety of roles in every sector of the economy: From the law to energy and others. This talk will offer some historical context for the advent of machine learning, discussion around its impact on industry and employment...

11:50am - 12:40pm

by John Langford
Leading Machine Learning Researcher, Vowpal Wabbit Contributor

The difference between a machine learning toolkit and a machine learning system is mechanisms for generating data and effectively deploying a model. Vowpal Wabbit ( is an online machine learning system which has been deployed and used in many companies. Having a complete data lifecycle prevents bugs, including very difficult to trace data bugs. It also makes the process of doing machine learning much faster...

1:40pm - 2:30pm

by Simon Chan
Co-Founder @PredictionIO & Senior Director of Product Management @Salesforce

Building a successful cloud-based A.I. dev platform on top of an open-source machine learning project is more complicated than one would imagine. Simply offering a hosted version of the project is hardly the answer. The secret to success is to differentiate the needs between the open-source users and the potential SaaS users. Oftentimes, they are of different species.

With years of...

2:55pm - 3:45pm

by Edo Liberty
Head of Yahoo's Independent Research

Machine learning and data mining, as a whole, try to distill patterns from large amounts of past data in the hopes of predicting future event. This model is problematic when predictions cause actions which influence the future or when the future cannot be assumed to be like the past. This is true, for example, in most security or abuse prevention applications. In the online model, one operates in an adaptive mode without assumptions of data stochasticity. The goal is to act in a way that is...

4:10pm - 5:00pm

by Richard Kasperowski
Author of The Core Protocols: A Guide to Greatness

Open Space
5:25pm - 6:15pm

by Suman Deb Roy
Lead Data Scientist @betaworks

The impact of machine learning solutions hinge on three entities working in cadence: data, software systems and humans-in-the-loop. At Betaworks, there are different companies/projects in different markets and in different stages of their growth cycle. The data team must work with natural language and news data, audio signals, gifs, images and videos, gaming data, very large social graphs and weather data - driving and supporting vastly disparate plus...


Monday, 13 June

Tuesday, 14 June

Wednesday, 15 June