18:45 Doors open. You will need to be escorted to the 3rd floor by elevator
19:00 Food, & Drinks
19:20 Welcome - Sandeep Ghael and Berni Schiefer
19:30 Creating an end-to-end Recommender System with Spark and Elasticsearch - Nick Pentreath (IBM Spark Technology Center)
There are many resources available for building basic recommendation models using Apache Spark, but how does a practitioner go from the basics to creating an end-to-end machine learning system, including deployment and management of models for real-time serving? In this session, we will demonstrate how to build such a system based on Spark ML and Elasticsearch. In particular, we will focus on how to go from data ingestion to model training to real-time predictive system.
20:00 Real-time, semi-supervised, learning: A look at online learning for robotics use cases - J White Bear (IBM Spark Technology Center)
In this talk, we explore practical and theoretical implications and implementations for real-time, semi-supervised learning on distributed infrastructure. We introduce a potential framework for examining these problems and investigate several robotics use cases including data association, path planning, and multi-robot scenarios. Our methodology demonstrates how moving away from purely embedded approaches towards more modern systems allows for greater flexibility and optimization of key heuristics in ongoing robotics research. We will also touch on a few interesting challenges for the next generation of machine learning.
20:30 Deep Learning in the cloud - Rania Khalaf (IBM Watson Research)
Deep Learning is causing quite a stir in the community. Join us to talk about deep learning, what makes it hard, what makes it attractive, and how a deep learning service makes it easy for developers and data scientists to train their deep learning models in their framework of choice without having to worry about what lies beneath their networks and their data. Under the hood, a cloud fabric supports the deep learning frameworks and takes advantage of accelerators like GPUs.
21:30 Adios 💤💤💤