ML pipelines for research: Stop doing R|D, start doing R&D

In this talk, we will harness the pipeline concept towards manageable high throughput experimentation in ML/DL research. We will make a distinction between top-down pipelines used in production and a bottom-up design that we propose for researchers. We make the claim that using such a design principle mitigates some of the problematic aspects of moving from research to development

Agenda:

11:40 am - 11:50 am Arrival and socializing
11:50 am - 12:00 pm Opening
12:00 pm - 1:50 pm Ariel Biller, "ML pipelines for research: Stop doing R|D, start doing R&D"
1:50 pm - 2:00 pm Q&A

About Ariel Biller:

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 (ClearML Self-Deployed), 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 AllegroAi.

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.

https://us02web.zoom.us/webinar/register/WN_RkbXsBiJRPuTUEZ0DBLeeg

Webinar ID: 892 2826 1191