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MLOps Working Group

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Attributes

Property Value
Date Created 2021-08-04
Target End Date -
Slack #wg_mlops (only accessible from within the company)
Google Doc MLOPs Working Group Agenda (only accessible from within the company)
Issue Label ~WorkingGroup::MLOps

Goals

This Working Group has the following goals:

  1. Begin defining and refine the results driven business value stream of MLOps at GitLab.
  2. Formalize the processes related to provenance, storage and access of GitLab.com production data for the purpose of model training.
  3. Share knowledge and determine best practices for hyper-parameter tuning, retraining, versioning, and deploying new ML models
  4. Determine best practices for benchmarking models built by different frameworks/libraries for different use cases in terms of accuracy and performance, and how to do it in a continuous basis.
  5. Determine how to distribute machine learning models on self-hosted instances
  6. Define a security/legal process for security-related ML models and data pre-processing

Definitions

What is MLOps?

As per Wikipedia, MLOps or ML Ops is a set of practices that aims to deploy and maintain machine learning models in production reliably and efficiently.

Read more about the topic area from the links below:

Exit Criteria

The charter of this working group is to bridge the gap between different teams that are building ML products at GitLab by discussing overlapping architectural concerns:

  1. Creation of a mission statement that the MLOps Working Group operates under, added to the handbook.
  2. Create a handbook page discussing a deep analysis of the tooling for ML out there and a proof-of-concept framework using an ideal pathway that GitLab teams can refer to and easily extend when kicking off ML-related projects.
  3. Creation of helpers/libraries (presumably in Python) that can be shared across teams and used for the purposes of similar tasks e.g. data access and storage, data pre-processing.

Roles and Responsibilities

Working Group Role Person Title
Executive Sponsor Laurence Bierner Director, Security Engineering & Research
Facilitator Alex Groleau Security Automation Manager
Functional Lead Roger Ostrander Senior Security Engineer, Trust & Safety
Functional Lead Alexander Chueshev Senior Backend Engineer, Applied ML
Functional Lead Taylor McCaslin Principal Product Manager, Secure
Functional Lead David DeSanto Senior Director, Product Management - Dev & Sec
Functional Lead Ethan Urie Senior Backend Engineer, Security Automation
Functional Lead Jayson Salazar Senior Security Engineer, Security Automation
Functional Lead Juliet Wanjohi Security Engineer, Security Automation
Functional Lead Eduardo Bonet Staff Full Stack Engineer - MLOps, SEG
Member Alexander Dietrich Senior Security Engineer, Security Automation
Member Charl De Wit Security Manager, Trust & Safety
Member Wayne Haber Engineering director
Member Bartek Marnane VP, Incubation Engineering
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