Krakow, Poland, 31 May - 2 June 2023
Developing a performing machine learning model is not enough. To ensure the proper usage of the model, we need to care about its deployment, maintenance, quality, delivery time and product management aspects. Good development practices, which are well known and adopted by the developers community, are sometimes forgotten by Data Scientists. We are going to cover 6 practices that each Data team can implement starting tomorrow.
Passionate about Data and sharing knowledge. Head of Data Science in Hymaïa, based in Paris.
Involved in multiple phases of Data products including development of Data Science platforms, data engineering, implementation of ML algorithms and activation of business use cases.
Graduated from the Polytechnic University of Catalonia, from the University of Lyon 2 in Data Mining and Knowledge Management and from the Warsaw School of Economics in Quantitative Methods in Economics and Information Systems.
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