Practical Machine Learning

Location: Broadway Ballroom South Center, 6th fl.

Day of week: Wednesday

Machine learning will soon have a profound effect on every industry on the planet. This revitalized wave generates huge demands and challenges for software developers with this expertise. The Practical Machine Learning track will focus on how developers can successfully build real world machine learning models based on the proven techniques using viable APIs and frameworks. Since its critical to using machine learning in our applications, we’ll also cover the best practices for collecting and preprocessing data, choosing and building models; these are some of the biggest challenges in putting machine learning in production.

Track Host:
Zoran Sevarac
Java and Neural Network Expert, Creator @Neuroph, & Founder @DeepNetts

Zoran Sevarac is software developer, AI researcher, entreprenuer and university professor.  He works at AI Lab at University of Belgrade, and he is CEO of deep learning startup Deep Netts. He is a founder of popular open source educational neural network software Neuroph, which has won prestigious Duke Choice Award. He is a member of Java Champions program and JCP Expert group for visual recognition. His main interests include software engineering, Java, machine learning and deep learning.

10:35am - 11:25am

by Zoran Sevarac
Java and Neural Network Expert, Creator @Neuroph, & Founder @DeepNetts

Application performance has direct impact on business and scaling ability. Performance tuning usually involves periodically setting a number of parameters that control run-time environment including CPU, memory, threading, garbage collection, etc.

In this session we present our experience and best practice for autonomous, continuous application performance tuning using deep learning.  The participants will learn how to build deep learning models in order to...

11:50am - 12:40pm

by Raghav Ramesh
Engineering systems for real-time predictions @DoorDash

Today, applying machine learning to drive business value in a company requires a lot more than figuring out the right algorithm to use; it requires tools and systems to manage the entire machine learning product lifecycle. For instance, we need systems to manage data pipelines, to monitor model performance and detect degradations, to analyze data quality and ensure consistency between training and prediction environments, to experiment with different versions of models, and to...

1:40pm - 2:30pm

by Golestan (Sally) Radwan
PhD in AI and Computational Biology

As companies seek to "inject" Machine Learning into their existing operations, they inevitably face a number of challenges. But perhaps the greatest and most-overlooked one is the people challenge. How can a successful software engineer make a transition to become an equally capable Machine Learning or Data Engineer?
How do team dynamics change when ML-specific processes, tools and pipelines are introduced? How can an engineering manager or team lead navigate this transition to retain...

2:55pm - 3:45pm

by Mikhail Kourjanski
Lead Data Architect, Risk and Compliance Management Platform @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 assurance that the company’s transactions are secure. How does PayPal achieve this goal in today's complex environment filled with "high-level" fraudsters as well as constantly increasing customer demand?...

4:10pm - 5:00pm

by Karen Siers
Agile Coach at Kapture Technologies

5:25pm - 6:15pm

by Seth Katz
Senior Software Engineer, Operational Insights @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 real time environment where baseline behavior changes over time. Those are all problems humans are good at, but state of the art machine learning approaches often struggle with.