Keymakr Data Labeling
Michael Seldin has extensive work experience in the software engineering field. Michael co-founded and served as CTO for Keylabs Annotation Platform and Keymakr A.E.M. ltd. Prior to that, they worked at Hewlett Packard Enterprise as a Senior Software Engineer and Features Lead. Michael also has experience as a Software Engineer at NDS, where they developed test tools and embedded software, conducted code profiling and optimization, and worked on system integration. Additionally, they worked as an Independent Software Engineer and Consultant for Stream, where they developed applications for the .Net platform and designed organization management applications. Michael also freelanced as a Software Engineer for Press-sense. Earlier in their career, they were a Software Developer and System Engineer at IDF, focusing on tools development, database management, web application development, and network administration. Michael began their career at NDS as a Software Engineer, specializing in GUI and test tools development.
Michael Seldin completed their BSc in Computer Science from Hadassah College between 2005 and 2008. Before that, they attended Hebrew University Secondary School (Leyada) and studied Computer Science. Additionally, they obtained a Certificate of Completion for 23 hours of Android development training from AnDevCon: The Android Developer Conference in 2014.
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Keymakr Data Labeling
Keymakr provides services of advanced data collection for training of Convolutional Neural Networks and Deep Learning Artificial Intelligence systems. Our experts in Data Collection team specialize in collecting and constructing high quality custom training datasets for deep learning algorithms. The company has the technological infrastructure and knowledge for efficiently collecting, sorting and verifying data from various data sources according to variable and individually suited parameters in accordance with specific requests of the customer. Our goal is to provide the customer with the best custom datasets that will improve ML system accuracy, efficiency and reduce overhead from dataset creation stage.