Ariel Biller

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 (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.

Build, Buy and the Golden Ratio: How Theator scaled up its continuous training framework
Wednesday, January 20 | 
10:40 AM - 
11:10 AM

Theator is a Surgical Intelligence Platform. It provides revolutionary personalized analytics on lengthy surgical operation videos. The deployed client-side inference pipeline consists of multiple deep learning models, so no off-the-shelf solutions truly fit the need for full CI/CD/CT. Building such critical infrastructure within a startup’s constraints would be impossible if not for existing MLOps solutions. In the current landscape, ClearML offered theator unique precursors to realize the needed designs with unprecedented integration ease.

In this session, we will make a brief – but deep – dive into Theator’s continuous training and inference pipelines, and detail the exciting interplay between in-house and provided. The topics will span from hybrid orchestration through pipeline design, and finally, advanced dataset management complying with the privacy requirements.

ClearML: Your R&D on MLOps! From zero to hero in two lines of code
Tuesday, January 26 | 
01:00 PM - 
02:00 PM

ClearML (formerly Allegro Trains) is the open-source platform that automates and simplifies developing and managing machine learning solutions for thousands of data science teams worldwide. It is now also available as a free managed service for small teams. This workshop focuses on R&D-oriented MLOps capabilities. You will learn how to easily integrate ClearML into your workflows and introduce reproducibility and automation. Additionally, we will share battle-tested productivity-boosting tips for pros and beginners alike.

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