MLOps Working Group

1. You are here:
2. Meet the GitLab team
3. Organizational Structure
4. Working Groups
5. MLOps Working Group

On this page

• Attributes
• Goals
• Definitions
  • What is MLOps?
  • Related GitLab Documentation
  • Related GitLab projects
  • Related ML Slack channels
• Exit Criteria
• Outcome
• Roles and Responsibilities

Attributes

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

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-managed 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:

• MLOps Wikipedia
• MLOps: Continuous delivery and automation pipelines in machine learning
• Machine Learning Operations
• MLOps Slack Group

Related GitLab Documentation

• MLOps Single-Engineer Group
• MLOps Primer
• MLOps Exploration
• Product Stage Direction - ModelOps
• Draft Group Direction - MLOps
• Group Direction - AI Assisted
• AI Assisted Group

Related GitLab projects

• Spamcheck
  • Architecture Diagram
• Tanuki-Stan
• UnReview
  • Architecture Diagram

Related ML Slack channels

• #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.

Outcome

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 Assisted
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 Assisted & Anti-abuse
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