EVENT - Wednesday
When6:00 PM - 9:00 PM
DSF Meetup with Liberty Global
Join Data Science Festival – London in partnership with Liberty Global. July 17th, for an evening of infrastructure, data preparation, machine learning supported as a service and automated delivery.
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 Liberty Global on July 17th 2019, the ballot will be drawn on the 12th July 2019. Those randomly selected will then be e-mailed a Universe ticket for the event, with the joining details.
If you get an allocated Universe 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.
PLEASE NOTE REGISTERING ON MEETUP DOES NOT GUARANTEE YOU ENTRY TO THIS EVENT.
Please click here to apply for a ticket: GET TICKETS
6:00pm: Doors open
6:30pm: Jose Del Rio
7:45pm: Caroline Zimmermann
Jose Del Rio – Infrastructure, data preparation, machine learning supported as a service and automated delivery.
Summary: Ease of access to the range of contents and services provided by any company is fundamental in order to satisfy the increasing requirements of the customers. To achieve this goal we are going to explore how we can use NLP, recommendation systems and other current technologies applied to user commands to improve the support of complex queries as well as to provide a degree of personalisation in the user queries. The initial approach to provide access to existing content tends to rely on the internal structure and classification embedded on the system holding the contents. This structure tends to be rigid and not always straight forward compared to the way a customer may think about a query. To overcome this gap between the internal structure and a user friendly interface it becomes necessary to create an extra layer that will seemingly translate the query as per the system requirements. More interestingly the discovery service must provide support for a wide range of inputs from the specific search of titles to general queries with genres, time frame, actors or rating…. This session will go over the infrastructure, data preparation, machine learning supported as a service and automated delivery.
Bio: Jose del Rio works as a Senior Data Scientist for Liberty Global in the Data Analytics team in London. After graduating with a Master’s Degree in Industrial Engineering at the University of Valladolid and being part of an exchange with the ENSAM University at Bordeaux, Jose gathered experience in a range of sectors including automotive, supply chain and media.
Caroline Zimmermann – From Soloist to Orchestral Player: How to Scale Big Data Products
Summary: When we first start out working on data products (a
dashboard, a predictive model), we’re often in a prototype environment
— or at least a relatively simple one — where our success is largely
dependent on our skills as a solo player. As long as we master our
tools, we do a good job creating a useful viz or an accurate model.
However, when we try to scale that model, we may find that we are far
less successful. This is because scaling data products requires not
just mastering our own technical skills, but orchestrating lots of
other moving parts — the business users, the data architecture and
engineering teams, the infrastructure and software we run our models
on. Being a maestro, making music out of all those different
instruments, is a very different skillset than simply playing the
piano, so to speak. This talk takes you on BMG’s journey from soloist
to maestro, and what it took to make that transition successfully.
Bio: I lead the data team (anayltics, data science, data engineering)
at BMG (Bertelsmann Music Group), the world’s fourth-largest music
company. Before that, I worked on various analytics initiatives across
different media businesses within Bertelsmann’s portfolio (they are
the largest media company in Europe). Previous to that, I got my MBA,
where I focused on stats and big data coursework. Before then, I was a