PAPIs.io 2015 Preliminary Program Announced

As we have blogged about before, PAPIs.io 2015 is taking place in Sydney this year on the 6th and 7th of August right before the KDD conference. As a founding member and sponsor, BigML is looking forward to this year’s event. PAPIs.io is unique in that it has been able to bring together data scientists, developers and practitioners from large tech companies to leading startups and prominent educational institutions from around the globe to discuss all aspects of Predictive APIs and Predictive Apps. The very hands on and interactive approach of the agenda is centered on addressing the challenges of building real-world predictive applications based on a growing number of Predictive APIs that are making Machine Learning more and more accessible to developers. As a bonus, this year’s event will also introduce a technical track. Our enthusiasm is only elevated further after seeing today’s preliminary conference program announcement, which exhibits great diversity in terms of the speakers and the topics to be covered.

Here are some preliminary program highlights:

• Big Wins with Small Data: PredictionIO in Ecommerce (David Jones, Resolve Digital)
  There’s a lot of noise about big data and cutting edge algorithms optimisations. Returning to the basics, this presentation shows you might not need as much data as you think to get real world benefits. Learn about machine learning in ecommerce, PredictionIO and how we used off the shelf, well implemented algorithms to get a 71% increase in revenue with an online wine retailer.
• Open Sourcing a Predictive API (Alex Housley, Seldon)
  After operating for three years as a “black box” predictive API, Seldon recently open-sourced it’s entire predictive stack. Alex will talk about Seldon’s journey from closed to open: the challenges and pitfalls, architectural considerations, case studies, changes to business models, and new opportunities for partnership across the full stack – between both open and closed technology providers.
• Deploying Predictive Models with the Actor Framework (Brian Gawalt, Upwork)
  Build a better, faster, more efficient predictive API with the Actor model of programming. Latency, logging, full utilization are all easily handled with this framework. Upwork (formerly Elance-oDesk) freelancer availability model — anticipating who’s looking for work right now — is now a real-time service, without costly or complicated build-out of our stack or our datacenter, thanks to the Actor model.
• Protocols and Structures for Inference: A RESTful API for Machine Learning (James Montgomery, University of Tasmania)
  Diversity in machine learning APIs works against realizing machine learning’s full potential by making it difficult to compose multiple algorithms. This paper introduces the Protocols and Structures for Inference (PSI) service architecture and specification for presenting learning algorithms and data as RESTful web resources that are accessible via a common but flexible and extensible interface. This is joint work with Dr. Mark Reid of the Australian National University and NICTA and Dr. Barry Drake of Canon Information Systems Research Australia.
• Large scale predictive analytics for anomaly detection (Nicolas Hohn, Guavus Inc.)
  The focus will be on anomaly detection for network data streams, where the aim is to predict a distribution of future values and flag unlikely situations. Challenges both in terms of data science and engineering will be discussed, such as the accuracy, robustness and scalability of the prediction API. An example of a production deployment will also be discussed.
• AzureML: Anatomy of a machine learning service (Sharat Chikkerur, Microsoft)
  Describing AzureML: a web service enabling software developers and data scientists to build predictive applications including. Will outline the design principles, system design and lessons learned in building such a system.

• Building Machine Learning Models for Predictive Maintenance Applications (Yan Zhang, Microsoft)
  This talk introduces the landscape and challenges of predictive maintenance applications in the industry, illustrates how to formulate (data labeling and feature engineering) the problem with three machine learning models (regression, binary classification, multi-class classification), and showcases how the models can be conveniently trained and compared with different algorithms in Azure ML.

There will also be a panel discussion moderated by Mark Reid of ANU/NICTA.

If you are also attending KDD and want to kill two birds with one stone while you will have travelled all the way Down Under, there is no better alternative than attending PAPIs.io 2015 and rubbing shoulders with some of the most notable movers and shakers in the Machine Learning world in a more cozy and comfortable setting. You can follow subsequent announcements on PAPIs.io on Twitter. Hope to see you all in Sydney!