Put AI to Work
April 15-18, 2019
New York, NY

Turn devices into data scientists—at the edge

Simon Crosby (SWIM.AI)
1:50pm2:30pm Wednesday, April 17, 2019
Implementing AI
Location: Regent Parlor
Secondary topics:  Edge computing and Hardware
Average rating: ****.
(4.67, 3 ratings)

Who is this presentation for?

  • Industry leaders and data scientists



Prerequisite knowledge

  • Basic knowledge of big data, edge computing, and artificial intelligence architectures

What you'll learn

  • Discover a new architecture for edge intelligence
  • Learn how edge intelligence can frequently be accomplished on the fly on streaming data


Today’s approach to processing streaming data is based on legacy big-data centric architectures, the cloud, and the assumption that organizations have access to data scientists to make sense of it all—leaving organizations increasingly overwhelmed.

Simon Crosby shares a new architecture for edge intelligence that turns this thinking on its head. Edge intelligence (encompassing analytics, learning and prediction, and edge computing) can frequently be accomplished on the fly on streaming data, cheaply, at the edge, without data scientists. Simon demonstrates how you can save up to $5,000 a month in cloud processing and storage costs while delivering accurate predictions that can transform outcomes, using well-established architectural pillars, such as the distributed actor model, to process voluminous real-time data at the edge, along with the rich commons of open source analytics and learning tools like Flink and Spark, on nothing more than a $200 device such as an NVIDIA Jetson.

The key insight is to use streaming data to build a digital twin model on the fly at the edge, avoiding a ton of complexity and infrastructure costs. Instead, a user defines the entities in their environment (e.g., traffic intersections, compressors, or assembly robots) that deliver data. Using the stateful distributed actor model, you can dynamically build a digital twin (actor) model of the real-world from the data, linking twins based on their relationships. Each digital twin reduces, labels, and analyzes its data and self-trains a machine learning model to predict future performance, at the edge, discarding the original data. This method needs only a tiny fraction of the resources of a big data solution and delivers results in real time. As a result, it bypasses the dev, ops, and data science challenges of edge intelligence, effectively turning devices into data scientists—or at least, building data science twins for entities in the real world.

Photo of Simon Crosby

Simon Crosby


Simon Crosby is CTO at SWIM.AI, an edge intelligence software vendor that focuses on edge-based learning for fast data. He cofounded Bromium in 2010 and now serves as a strategic advisor. Previously, he was the CTO of the Data Center and Cloud Division at Citrix Systems; founder, CTO, and vice president of strategy and corporate development at XenSource; and a principal engineer at Intel, as well as a faculty member at Cambridge University, where he led the research on network performance and control and multimedia operating systems. Simon is an equity partner at DCVC, serves on the board of Cambridge in America, and is an investor in and advisor to numerous startups. He’s the author of 35 research papers and patents on a number of data center and networking topics, including security, network and server virtualization, and resource optimization and performance. He holds a PhD in computer science from the University of Cambridge, an MSc from the University of Stellenbosch, South Africa, and a BSc (with honors) in computer science and mathematics from the University of Cape Town, South Africa.