Forged in fire

Project management lessons from the frontline

Chris Beeley

22 November 2024

Intro

  • I got a new job two years ago
  • Learning ++
  • I want to tell the story
  • I’ve drawn from three wells:
    • Agile/ Scrum
    • MLOps
    • The skills and experience of those around me
  • This talk will not properly define scrum or MLOps

The New Hospital Programme

  • January 2023: Development phase
  • April-ish: Thinking about deployment
  • October-ish: Model is on its way to full production and much remains to do

Operational mode

  • The team was growing in November
  • There were two priorities
    • Increase bus factor for Tom (which I’ll come back to)
    • Deploy the first production ready version

The first deployed version

  • December-ish: Many needs that were not anticipated
  • This first release kicked off lots of other work
  • The second release kicked off lots of other work
  • It was very hard to do any long term planning
  • By March we were confused and tired (and it was all my fault)

Scrum

  • We are not doing “proper” scrum
  • Product owner, scrum master, everyone else
  • Five week sprints with a one week recovery run between each one
  • Sprint planning, sprint catchup, sprint retro

Sprint retro

  • What went well, what could have gone better, and what to improve next time
  • Looking at process, not blaming individuals
  • Requires maturity and trust to bring up issues, and to respond to them in a constructive way
  • Should agree at the end on one process improvement which goes in the next sprint
  • We’ve had some really, really good retros and I think it’s a really important process for a team

What did scrum give the team?

  • Simultaneous releases of linked repos
  • The team works autonomously in the sprint
  • Better conversations about “no”
  • The planning and retro process improves the team’s processes, not just the code

Product owner

  • My lessson- get out the way
  • A better connection between high level and low level planning
  • Clear release dates and responsibilities
  • Clear what I should be doing

Agility

  • This project was agile whether we liked it or not!
  • My 2022 agile definition:
    • Customers can’t make up their minds
    • It’s hard to design software all at once
    • Continuous delivery keeps customers happy

Some highlights from The agile manifesto

  • “Welcome changing requirements, even late in development”
  • “At regular intervals, the team reflects on how to become more effective, then tunes and adjusts its behavior accordingly”
  • “Continuous attention to technical excellence and good design enhances agility”
  • “Simplicity- the art of maximizing the amount of work not done- is essential”

How scrum helped

  • Those shaping the project wanted to be able to make quick changes- and see the long term plan
  • Agility is a mindset, a mode of practice
  • If anything we were actually too agile
  • Being agile is all about being able to review and make decisions frequently
  • But it isn’t about changing what you’re doing all the time
  • Good code and good teams are ready to change direction- whether they change or not

MLOps

  • Like DevOps, but for ML 🙂
  • Deploying, updating, and monitoring models in production
  • This isn’t a very ML-y project but much of it still applies (not all though)
  • We have a set of customers using a model and we want to push updates to that model monthly
  • We also want to be able to rerun old models (for audit purposes)

Levels

  • Levels of MLOps- 0, 1, 2
  • Continuous integration and continuous delivery
  • There are various finicky differences in definition
  • I’ll just talk about the bottom, top, and where we are

Level 0 MLOps

  • You process your data in a Jupyter notebook
  • You make a model on a Jupyter notebook
  • You give your code to the engineers who attempt to recreate your data and environment

Level 2 MLOps

  • Data is processed in a pipeline
  • Code is continously integrated and deployed to production
  • Development, staging, and production environments are configured identically

Where are we?

  • We’re sort of level 1-ish
  • Data pipelines are definitely RAP! (and recently quicker)
  • We have a dev environment
  • Merge to main deploys to dev, GitHub release deploys to prod

QA, QA, QA

  • QA is super, super, super important
  • QA of data in particular- it’s also time consuming (trying to RAP!)
  • QA is done at regular staging points
  • QA is done continuously

Other stuff I learned along the way

  • It doesn’t matter who’s right, it matters how people feel
  • Transparency isn’t just good for robust code and analysis- it builds relationships
  • Bus factor, bus factor, bus factor
  • Documentation, documentation, documentation

The future

  • Open source
  • Mo’ team members, mo’ conscious work on “teaming” and collaboration

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