Abstract: 

This workshop will harness the pipeline concept towards manageable high throughput experimentation in ML/DL research. We will distinguish between top-down pipelines used in production and a bottom-up design that we propose for researchers. We will see how to take a “conventional” flower detection example and employ the bottom-up design principle. While integrating typical research steps, we will mitigate some of the problematic aspects of moving from research to development.

Bio: 

Researcher first, developer second, in the last 5 years Ariel worked on various projects from the realms of quantum chemistry, massively-parallel supercomputing and deep-learning computer-vision. With AllegroAi, he helped build an open-source R&D platform (Allegro Trains), and later went on to lead a data-first transition for a revolutionary nanochemistry startup (StoreDot). Answering his calling to spread the word on state-of-the-art research best practices, He recently took up the mantle of Evangelist at ClearML. Ariel received his PhD in Chemistry in 2014 from the Weizmann Institute of Science. With a broad experience in computational research, he made the transition to the bustling startup scene of Tel-Aviv, and to cutting-edge Deep Learning research.