Welcome to the "Deep Learning in Practice" learning series, presented by Allegro AI
. This series is focused on methodologies and tools for machine and deep-learning(ML/DL) projects.
In the 5th session, we will focus on practical experiences and practises on MLOps
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.
All sessions of the series:
Dec 2nd: The Hitchhiker’s Guide to Hyperparameter Optimization. Session 4
Nov 11th: ML Pipelines for Research: This is the Way. Session 3
Oct 14th: The Fundamentals of Research-MLOps. Session 2
Sep 23rd: Insights on Data Challenge in Deep Learning Projects. Session 1
Ariel Biller (AllegroAI)
Ariel recently took up the mantle of Evangelist at AllegroAI. He is enthusiastic about the rapid evolution of MLOps used in academic and industrial research.
Ariel received his Ph.D. 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.
In the past 5 years, Ariel worked on various projects from the realms of - quantum chemistry, massively-parallel supercomputing, deep-learning computer-vision, and even the data science of ultra-fast-charging batteries.