Track: Solving Software Engineering Problems with Machine Learning

9:00am - 9:40am

Policing the Capital Markets with ML

Cliff Click talks about SCORE, a solution for doing Trade Surveillance using H2O, Machine Learning, and a whole lot of domain expertise and data munging. SCORE pulls in private and public market data and in a few minutes will search it for all sorts of bad behavior and downright illegal activity. It then filters down the billions of rows of data down to human scale with some great visualizations.

10:00am - 10:40am

Document Digitization: Rethinking OCR with Machine Learning

When you think about Document digitisation from a business optimization process perspective, just performing OCR does not truly solve the problem. We at omni:us are building AI systems to support the insurance industry by handling claims. In order to achieve this we are performing various human-esque activities on so many different types of documents like page / document classification, information extraction, semantic understanding to name few. These activities helping in delivering structured information from highly unstructured documents. This structured information is further used in performing activities such as fraud detection, validation and automated claims settlement. 

This talk will outline:

• The problems and approaches we faced when building deep learning networks to solve problems in the information extraction process.
• Thought process on why and how we chose certain deep learning strategies
• The requirement for supervised learning
• Limitations of deep learning networks
• Planning and executing research activities in short cycles
• Evolution of team structures to support AI product building
• Engineering practises required in building AI systems. 

11:00am - 11:40am

Improving Developer Productivity with Visual Studio Intellisense

In this session, we'll discuss how PM, engineering, and data science came together to build Visual Studio IntelliCode, which delivers context-aware code completion suggestions (IntelliSense). We'll take you through our journey of customer research, model tuning, and an overview of our engineering layout to showcase how you can think about adding AI/ML capabilities to existing tools.

12:00pm - 12:40pm

Ludwig: A Code-Free Deep Learning Toolbox

The talk will introduce Ludwig, a deep learning toolbox that allows to train models and to use them for prediction without the need to write code. It is unique in its ability to help make deep learning easier to understand for non-experts and enable faster model improvement iteration cycles for experienced machine learning developers and researchers alike. By using Ludwig, experts and researchers can simplify the prototyping process and streamline data processing so that they can focus on developing deep learning architectures.

1:40pm - 2:20pm

Lessons Developing Conversational AI Interfaces

This talk will cover building conversational AI using deep learning technologies and lessons learnt in developing conversational interfaces. The first part of the talk will describe recent advances in deep learning that has led to tremendous progress in natural language processing and is making conversational AI a reality. Conversational AI includes intent classification, sequence labeling, understanding dialogs and context, and coming up with responses to users messages. The second part of the talk will address lessons learned developing conversational interfaces. A conversational interface needs to be personable in addressing, adaptive in understanding, and available with many different supported tasks. Overall, key take aways from the talk will be better understanding of (1) deep learning techniques for natural language processing and (2) interaction patterns for building a good conversational interface.

2:40pm - 3:20pm

Panel: First Steps with Machine Learning

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 you systems and how they solved challenges. Eager to deploy ML and have questions? This is a forum to discuss, learn, and help crystalize that roadmap. Join Cliff Click, Soups Ranjan, and the track speakers as they discuss first principles adding ML to your systems.