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.
by David Beyer
Investor at Amplify Partners, Co-founder @ Chartio.com, Founding Team @ Patients Know Best
by Rob Witoff
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...
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 (http://hunch.net/~vw) 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...
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...
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...
by Richard Kasperowski
Author of The Core Protocols: A Guide to Greatness
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
Architectures You've Always Wondered About
Case studies from: Google, Linkedin, Alibaba, Twitter, and more...
Stream Processing @ Scale
Technologies and techniques to handle ever increasing data streams
Culture As Differentiator
Stories of companies and team for whom engineering culture is a differentiator - in delivering faster, in attracting better talent, and in making their businesses more successful.
Practical DevOps for Cloud Architectures
Real-world lessons and practices that enable the devops nirvana of operating what you build
Incredible Power of an Open-Sourced .NET
.NET is more than you may think. From Rx to C# 7 designed in the open, learn more about the power of open source .NET
Sponsored Solutions Track 1
Tuesday, 14 June
Better than Resilient: Antifragile
Failure is a constant in production systems, learn how to wield it to your advantage to build more robust systems.
Innovations in Java and the Java Ecosystem
Cutting Edge Java Innovations for the Real World
Modern CS in the Real World
Real-world Industry adoption of modern CS ideas
Containers: From Dev to Prod
Beyond the buzz and into the how and why of running containers in production
Security War Stories
Expert-level security track led by well known and respected leaders in the field
Sponsored Solutions Track 2
Wednesday, 15 June
Microservices and Monoliths
Practical lessons on services. Asks the question when and when to NOT go with Microservices?
Modern API Architecture - Tools, Methods, Tactics
API-based application development, and the tooling and techniques to support effectively working with APIs in the small or at scale. Using internal and external APIs
Commoditized Machine Learning
Barriers to entry for applied ML are lower than ever before, jumpstart your journey
Keeping life in balance is always a challenge. Learning lifehacks
Sponsored Solutions Track 3