“Chicken or the Egg” in MLOPs: The Case of the DIY Feature Store

Wednesday 11 August 2021

Starts 18:00 PM

Finishes 19:00 PM


Organized by ODSC Lisbon Data Science


Venue: Online/Virtual

Address: Online event on your device
8600 Lisboa

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About this event

To access this webinar, please register here: https://app.aiplus.training/courses/chicken-or-the-egg-in-mlops-the-case-of-the-diy-feature-store

Topic: “Chicken or the Egg” in MLOPs: The Case of the DIY Feature Store

Speaker: Ariel Biller, Evangelist at ClearML

Researcher first, developer second. Over the last 5 years, Ariel has worked on various projects; from the realms of quantum chemistry, massively-parallel supercomputing to 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, he recently took up the mantle of Evangelist at ClearML. Ariel received his PhD in Chemistry in 2014 from the Weizmann Institute of Science. Ariel recently made the transition to the bustling startup scene of Tel-Aviv, and to cutting-edge Deep Learning research.

Abstract:

While developing MLOps in an organization, one often encounters the “chicken or the egg” paradox: On one hand, a certain abstraction, workflow, or tool is hypothesized to be a good fit. On the other hand, there are often considerable integration efforts even before feasibility testing can commence.

In other words, you cannot know if the tool works unless you “buy in”.

The common solution is to “build” internal implementations which optimize for integration ease. Most of these “interim” solutions take a life on their own and will only be replaced once (if) they outlive their usefulness or another abstraction is adopted.

In this webinar, we will simulate this low-integration-cost process by building a feature-rich Feature Store for offline use using the ClearML Open Architecture Stack.

We will use this surprisingly simple process to understand the following:

How to iteratively add MLOps onto functioning code

How DataOps and MLOps can be used during R&D

How to involve Data Scientists in the feasibility testing stage

How ClearML core components provide a robust infrastructure to build upon

Finally, we will compare our own DIY Feature Store with off-the-shelf solutions and understand the gap between “in-house” and “store-bought”.

PS: This webinar expands upon the ClearSHOW episodes S02E04-S02E08

Prior knowledge needed:

“What is a feature store” (2 min video)

Basic pandas (we will be using a few lines from this

Jupyter notebooks

Environment requirements (in case you want to follow along):

ClearML installed (Free or self-hosted)

Jupyter server or colab notebook.

clearml-agent running (for automation)

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This page last updated Saturday 10 July 2021 at 14:11.

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