Track: Solving Software Engineering Problems with Machine Learning

Location: Cyril Magnin III

Day of week: Wednesday

Andrew Ng called AI "the new electricity." Just as electricity changed forever how we solved the engineering problems of the day, artificial intelligence/machine learning stands poised to reshape how we think of modern software engineering.

The Solving Software Engineering Problems with Machine Learning track looks at the interesting use cases and applications of machine learning in what we have thought of traditional software development. We'll uncover real-world experiences of software engineers working with data scientists to build products that leverage machine learning into their platforms. 

Topics we're pursuing include how machine learning is changing our development environments, how we can better make sense of large volumes of notifications/alerts, how the commoditization of machine learning tooling is making it more approachable for the "rest of us," and how machine learning can be used to optimize environments as diverse as the Kubernetes to entire data centers.

Track Host: Wes Reisz

Software/Technical Advisor C4Media & QCon Chair, previous Architect @HPE

Wes Reisz joined QCon in 2015 and leads QCon Editorial as the conference chair. Wes focuses his energies on providing a platform for practicing engineers to tell their war stories so innovative/early adopter stage engineers can learn, adopt, and, in many cases, challenge each other. Before joining the QCon Team, Wes held a variety of enterprise architecture and software development roles with HP. His focus with HP was around developing/federating identity, integration/development of Java stack applications, architecting portal/CM solutions, and delivering on mobility in places like US Army’s Human Resources Command (HRC), Army Recruiting Command, and Army Cadet Support Program. In 2002, Wes began teaching as an adjunct faculty member at the University of Louisville. He continues to teach 400-level web architecture and mobile development courses to undergraduates. He is currently teaching Mobile Application Development with Android.

9:00am - 9:40am

Developing a Machine Learning MVP @ThirdLove

Megan Cartwright, Director of Data Science @ThirdLove

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. 

 

Nischal Harohalli Padmanabha, VP of Engineering and Data Science at Omnius

11:00am - 11:40am

Machine Learning Presentation

12:00pm - 12:40pm

Machine Learning Presentation

1:40pm - 2:20pm

Solving Problems with Machine Learning Panel

2:40pm - 3:20pm

Machine Learning Presentation

2019 Tracks

  • Groking Timeseries & Sequential Data

    Techniques, practices, and approaches around time series and sequential data. Expect topics including image recognition, NLP/NLU, preprocess, & crunching of related algorithms.

  • Deep Learning in Practice

    Deep learning use cases around edge computing, deep learning for search, explainability, fairness, and perception.

Schedule

Learn how to apply machine learning and artificial intelligence in engineering

REGISTER. Save up to $105 before Feb 23rd.

Register