MLOps Working Group

Learn more about the GitLab MLOps Working Group attributes, goals, roles and responsibilities.


Property Value
Date Created 2021-08-04
Target End Date 2022-02-24
Slack #wg_mlops (only accessible from within the company)
Google Doc MLOPs Working Group Agenda (only accessible from within the company)
Board gitlab-org board
Issue Label ~WorkingGroup::MLOps


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 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-managed instances
  6. Define a security/legal process for security-related ML models and data pre-processing


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.

MLOps Vendiagram

Read more about the topic area from the links below:

  • #g_applied_ml
  • #g_machine-learning
  • #feed_tanuki-stan
  • #security-ml

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.


  1. The Working Group has brought together various teams that are working on different ways to leverage Machine Learning within GitLab the product and in their day-to-day work. It has given these teams a platform to discuss their mission and an opportunity to share knowledge on projects they are currently undertaking and what they are looking to achieve in the near future.
  2. We will move forward with the creation of a monthly MLOps meetup that focuses on sharing ideas and showcasing work done by different teams, in a less formalized structure, to enhance awareness across the company.

Roles and Responsibilities

Working Group Role Person Title
Executive Sponsor Laurence Bierner Director, Security Engineering
Facilitator Alex Groleau Security Automation Manager
Functional Lead Roger Ostrander Senior Security Engineer, Trust & Safety
Functional Lead Alexander Chueshev Senior Backend Engineer, AI Framework
Functional Lead Taylor McCaslin Group Manager, Product - Data Science
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
Functional Lead Monmayuri Ray Engineering Manager, AI Model Validation
Functional Lead Shawn Sichak Senior Security Engineer, Trust and Safety
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
Member Marshall Cottrell Strategy and Operations (Technical) Staff
Member Kelly Chen Analytics Manager at Finance
Member Jay Stemmer Manager, Analytics & Insights