Join Data Science Festival – London in partnership with SAS in September for two great speakers.

Please register for a ballot ticket here: Get Tickets

Due to the popularity of Data Science Festival events, we are now allocating event tickets via a random ballot. Registering here enters you into the ticket ballot for the Data Science Festival Event at SAS on September 12th, 2018, the ballot will be drawn on the 7th September 2018. Those randomly selected will then be e-mailed tickets for the event, with the joining details.

If you get an allocated ticket, please bring a copy of your paper ticket or your ticket on your phone to the event to check in with your QR code. Tickets are non-transferable.

SCHEDULE

6.00pm Guests arrive

6.30pm – Kayne Putman – Computer Vision applications with breakout and feedback from discussion

7.15pm – Break: drinks, food & networking

7.45pm – Kayne Putman & TBC – Computer Vision: The SAS approach

8.30pm – Networking

9.00pm – Close

Kayne Putman – Analytics Consultant

Summary: SAS see computer vision as one of the main analytical approaches encapsulated within artificial intelligence. The ability to apply image processing techniques, build deep learning models and deploy this insight in-batch, in-stream or at-the-edge is what is enabling businesses to drive new insight from unstructured image and video data.

In our two talks, we will initially highlight how computer vision has been fuelled by advancements in technology and the lifecycle of a typical computer vision project. We will then break out and think through how some aforementioned techniques could be used to deliver a real use-case. Finally, we will wrap-up by demonstrating the approaches SAS has undertaken for some of its customers to drive real business value.

Bio: Kayne has been working for SAS for over a year as an analytics consultant, but has six years of experience building analytical solutions to solve business problems for various organisations including British Airways, the NHS and GlaxoSmithKline. His previous role was leading a data science and innovation team, where the team explored the benefit of applying new technologies, new data sources and new analytical techniques to improve business decisions. Kayne graduated with an MSc in Operational Research and Applied Statistics.