PyData Global 2022

MLOps for the rest of us: A poor man's guide to putting models in production
12-02, 17:00–17:30 (UTC), Talk Track I

What if you're a two man machine learning team deploying models to users? What if you don't have a full blown team of Data Engineers working with you? What if nobody around you cares about making that nasty production data available in a pristine feature store? What if you don't even have time to build out your entire Machine Learning platform?

There must be a way to still deliver your ML model to users right? There must be way to deliver value.

In this session, I'll talk about how small teams address the problem of delivering ML-value to users. At a reasonable scale. I'll go over some misconceptions and lessons-learned from 4 years working with early-stage startups.

Summary & Objective

MLOps doesn't have to be a monster. There is a set of principles/rules/methodologies that one can follow to deliver Machine Learning models to production. And it all comes down to following some standard practices that have been used in Software development for years now.

The main objective of this talk is to give attendees some insights on how to take MLOPs into their own hands and get that model to production. This will be done through story telling (e.g., some MLOps stories from the trenches), and practical examples (from my experience).

By the end of the talk, attendees should have increased confidence that they can take the MLOPs problem, and solve it. At their own scale.

Outline

  • Cover slide
  • Speaker introduction (2 min)
  • What we'll talk about (2 min)
  • You're not LinkedIn (4 min)
    • The size of large tech companies
    • Examples of startups I worked with
    • Cost is a concern
    • Time to value is a concern
  • MLOPs (4 min)
    • It's all the rage
    • But what's the main goal here?
  • How to solve small scale ML Problems (15 min)
    • Communicating with the business
      • "We need an ML model"
    • The Data, what if you don't even have it?
      • Scrapping
      • Continuous learning
    • Start uber small
      • Packaging
      • MakeFile
      • Unit tests
      • Continuous integration/Delivery
    • Delivering models
      • FastAPI
      • Docker based services (eg., Cloud Run or similar)
    • Monitoring models
      • Evidently
      • Prediction logging
    • Making a platform
      • Cookiecutter
  • Key takeaways
    • You can go a long way with 2 or 3 tools
    • Make sure your ML Engineers know how to make software
    • Keep the main goal in mind

Prior knowledge

Basics of Python and Machine Learning

Prior Knowledge Expected –

Previous knowledge expected
Duarte Carmo

I'm a Technologist/hacker, born and raised in Portugal, now based in Copenhagen. My work lies in the intersection of Machine Learning, Data, Software Engineering, and People. I'm in love with Technology, and how it improves people's lives.

I help large scale companies and startups delivering value to users. I've worked with clients from all over the spectrum: from Public companies to YC startups and smaller. Currently, I run my own Machine Learning consulting shop.