Łukasz Lipiński

Łukasz Lipiński has been working in the tech industry for over a decade. Łukasz began their career in 2010 as a Software Engineer at Global Corporation. Łukasz then worked as a Software Engineer at Media Cyfrowe from 2011 to 2013, followed by a role as a Software Engineer at Estakada.pl from 2013 to 2014. In 2014, they became a Senior Software Engineer / Tech Lead at Adpilot. In 2016, they moved to Cloud Technologies S.A. as a Machine Learning Engineer. From 2018 to 2022, they worked as a Machine Learning Consultant / Data Scientist at Deep BI, Inc. and is currently employed as a Senior Data Scientist at Allegro.

Łukasz Lipiński obtained a Bachelor's degree in Computer Science from the Military University of Technology between 2007 and 2011. Łukasz then went on to receive a Master's degree in Information Technology from the Warsaw School of Computer Science between 2012 and 2014. In addition, Lipiński has obtained numerous certifications from Coursera, Yale University, and other institutions, including Machine Learning Data Lifecycle in Production and Machine Learning Modeling Pipelines in Production (2021), Introduction to Machine Learning in Production (2021), Custom and Distributed Training with TensorFlow (2021), Custom Models, Layers, and Loss Functions with TensorFlow (2020), Natural Language Processing with Sequence Models and Natural Language Processing with Probabilistic Models (2020), Natural Language Processing with Classification and Vector Spaces (2020), Financial Market (2020), Machine Learning with TensorFlow on Google Cloud Platform Specialization (2020), TensorFlow in Practice Specialization (2019), Natural Language Processing in TensorFlow (2019), Convolutional Neural Networks in TensorFlow (2019), Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning (2019), Guided Tour of Machine Learning in Finance (2018), Deep Learning Specialization (2018), Sequence Models (2018), Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization (2017), Neural Networks and Deep Learning (2017), and Structuring Machine Learning Projects (2017).