Past Meetup

#LondonAI Meetup @ Big Data LDN 2018

This Meetup is past

286 people went

West Hall, Olympia

Olympia, Kensington · London

How to find us

Please go to AI Lab Theatre

Location image of event venue


We will be hosting our November meetup in conjunction with Big Data LDN conference ( again this year.

Please note that all members will need to register free via this link to obtain entry

This will also give you access to the two days of Big Data LDN should you wish to attend.

- Welcoming Remarks by Joe Chow
- Talk 1: Massive scale 3D generation for e-commerce by Tushar Sharma (
- Talk 2: Which Data Science Platform is Best by Fazl Barez (RBS)
- Talk 3: Introduction to RAPIDS by John Harding (Nvidia)


Talk 1: Massive scale 3D generation for e-commerce

Humans have evolved to interact with and understand our 3D world. 3D interfaces are hugely appealing and intuitive to us, but right now the web is stuck in two dimensions. The main barrier to widespread use of 3D is the enormous cost and time required to build 3D content. While it takes less than a second to capture an image, it can take from hours to weeks to create a 3D model. envisions a future web powered by 3D content. To realise this, a paradigm shift away from tedious, manual content generation is needed. We are building a technology where 3D versions of real objects can be instantly created using natural and intuitive inputs: 1) a single photograph, 2) speech or textual descriptions or 3) inference from spatial context. On-demand 3D creation at affordable prices unleashes new opportunities for E-commerce, XR, and games.

Tushar is an engineer with extensive experience as a venture developer. He has spent over two years building successful start-ups from ground-up in Africa and Latin America which includes marketplaces, e-classifieds, and EdTech. Most recently, he was Director of Education and Enterprise at Blippar, a leading AI and AR firm.


Talk 2: Which Data Science Platform is Best by Fazl Barez

In the era of the internet where the amount of data has seen a considerable rise in size, the task of selecting machine learning tools to analyse these data can be costly, and the need for automated methods for data analysis has seen exponential growth for many sectors. The available tools have advantages and disadvantages, and usually, most have overlapping functionality. The traditional means for machine learning are becoming insufficient as we move towards distributed and real-time processing. To evaluate platforms, it is critical to have an understanding of what to look for. To that end, this talk shall provide a list of criteria for assessing platforms. This is done by starting from the beginning and looking at the capabilities of each platform as well as the extent to which the outputs of these platforms can be explained.

Fazl Barez is currently, a Data Scientist - Machine Learning Engineer at RBS. Prior to this Fazl did his masters thesis at the Data Lab - Innovation centre where he focused on the challenges of explainable Machine Learning in the context of data science platforms. Fazl’s main area of interest is predictive modeling and applications of behaviour science and data science.


Talk 3: Introduction to RAPIDS by Nvidia