The Fundamentals of Research-MLOps: Orchestration, Automation, Reproducibility

Oct 14, 10:00AM PDT(05:00PM GMT).
  • Free 259 Attendees
Welcome to the "Deep Learning in Practice" learning series, presented by AllegroAI. In collaboration with AllegroAI, we host a series of practical sessions focused on methodologies and tools for machine and deep-learning projects.

This is the 2nd session:
“MLOps”, a term coined less than 5 years ago, is often touted as “DevOps for ML”. This is a fitting ‘working definition’ since for a while now the emphasis is on enabling cluster-based (cloud or on-prem), containerized, end-to-end processes. The ‘new wave’ of MLOps redefines the target as providing the chain-of-custody in these processes. In other words, the provenance of the transition from data to deployed models. The core-mission of MLOps tools is thus the abstraction of this target.

Peculiarly, missing from both current and new definitions is the laborious process of research and development required prior to creating an end-to-end pipeline. Evidently, one can ‘carve’ specific capabilities from existing MLOps tools for, i.e. research, but because the orientation is towards ‘production’, most of the abstractions will tend to be ill-fitting. Which ones are still valid, and can be used to construct productivity-enhancing research MLOps?

Putting the researcher and her ability to experiment at the center, we recognize that the computational needs are unchanged from other disciplines: The need to orchestrate a large number of interdependent trials, in a reproducible and manageable way. The MLOps tools that enable this - are the most fundamental ones. From such, the road from research to production can be navigated using robust pipelines.

In this “best-practices” webinar, we will overview the “must-haves” and show examples of research-MLOPs that stand out: From easy remote execution, through magically reproducible setups, and even custom, reusable, research pipelines.

All sessions of the series:

  • Sep 23rd: Insights on The Data Challenge in Deep Learning Projects. Session 1
  • Ariel Biller

    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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