DSS: Data engineering – the unglamorous reality behind data science

As data scientists, we are naturally interested in developing fancy new machine learning models and clever analyses, but what does it really take to build data-intensive products? This seminar will showcase the unglamorous reality behind building successful data science solutions. How to collect and store large amounts of data in real time and make that available for analysis? How to annotate and label the data to train machine learning models? How to iterate to improve both the data and the models without losing track of different versions? Come and join the experts from four different companies and hear what they have learned over the years.

• The seminar will be held in English. Limited number of participants can join. Attending the webinar is free of charge, but registration is needed here:

NB! The registration is open until November 18 at 5 PM.

Agenda:

17:00 Kaur Alasoo and Riccardo Tommasini (University of Tartu): Welcome & introduction
17:20 Łukasz Grądzki (Bolt): “Data Platform at Bolt: Lessons from scaling data infrastructure in a hyper growth company”
17:45 Kristjan Eljand (Eesti Energia): “Labelling the labelled”
18:10 Rao Pärnpuu (Starship Technologies): “Using datasets to develop and globally operate self-driving robots”
18:35 Taivo Pungas: “Datasets: the source code of Software 2.0.”
19:00 Panel discussion