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?

Schedule:
?0:00 Kaur Alasoo & Riccardo Tommasini (University of Tartu Institute of Computer Science)
?16:06 Łukasz Grądzki (Bolt): Data Platform at Bolt: Lessons from scaling data infrastructure in a hyper growth company
?42:35 Kristjan Eljand (Eesti Energia): Labelling the labelled
?1:03:47 Rao Pärnpuu (Starship Technologies): Using datasets to develop and globally operate self-driving robots
?1:32:04 Taivo Pungas: Datasets: the source code of Software 2.0.
?2:08:56 Panel discussion

More information about our seminars: https://www.cs.ut.ee/en/industry-collaboration/data-science-seminars

Data Science Seminars are supported by the European Social Fund and University of Tartu ASTRA project PER ASPERA Doctoral School of Information and Communication Technologies.