How to track progress and collaborate in data science and machine learning projects
In this talk from the Machine Learning Conference, Kamil Kaczmarek and Jakub Czakon focus on practical guidelines and tips on how to set-up and maintain smooth collaboration in data science projects. Discover how to best track and collaborate in data science.
In this talk from the Machine Learning Conference, you will learn how you can have your work organized around creative iterations, reproducible and easy to share with anyone. You will see how to easily track the code, metrics, hyperparameters, learning curves, data versions and more.
Speakers Kamil Kaczmarek and Jakub Czakon draw upon their experience in the field and lessons learned from neptune.ml. They address the need for mutual communication between data scientists and business people to ensure a healthier organization that can be easily maintained and runs smoothly.
Kamil Kaczmarek Data Science and Machine Learning expert. Member of the winning team of the crowdAI “Mapping Challenge” competition in 2018. Co-founder of neptune.ml -> collaboration platform for DS/ML teams.
Previously systems neuroscience researcher who did research visits to Princeton University and Janelia Research Campus. He graduated from Adam Mickiewicz University (Poland) with Computer Science and Cognitive Science degrees.
Jakub Czakon is Senior Data Scientist at neptune.ml. He graduated Physics at University of Silesia in Katowice and Finance at University of Economics in Wroclaw.
He is a seasoned chess player holding a prestigious International Master title.
His data-driven mentality drove him to machine learning where he worked on various data science projects involving facial recognition, optical character recognition, cancer detection and classification, satellite image segmentation, text mining labor market data and many more. He was a member of the teams that won MICCAI Munich 2015 „Combined Imaging and Digital Pathology Classification Challenge”, won MICCAI Athens 2016 „Pet segmentation challenge using a data management and processing infrastructure” and won crowdAI “Mapping Challenge” competition in 2018.