ML Pipelines for Research: This is the Way


Nov 11, 10:00 AM PST
  • Virtual
  • 192 RSVP
Description
Speaker
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 3rd session, we will harness the pipeline concept towards manageable high throughput experimentation in ML/DL research.

Currently, complex pipelines are found in the field of ML in implementations of automated training and deployment of ML models. However, these pipelines and the code they encapsulate are rarely those that are used in the research stage. Moreover, existing research pipelines tend to be focused on the data preparation stage, and are mostly trivial afterward.

This represents several areas where we can do better:
* Easily “grow” automated multi-stage workflows from research code with minimal code changes.
* Frictionless executions of these pipelines on available resources.
* Minimizing re-writes when promoting code from research towards “production”

I will address suggestions for improvements in these, with specific examples from simple to intricate workflows in research.

In the previous webinar, we established how ensuring reproducibility in ML research enables automation, which in turn unlocks advanced MLOps (such as pipelines). Since these topics will be used for this webinar, it is recommended to refresh your memory with the recorded event.

All sessions of the series:

  • 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. 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.
    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.
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