Getting to the Right Team in an ML Project

I think getting at the data is a hell of an important thing. You know nothing about an ML project until you've seen the dataNothing at all. So finding them is a priority I think breaking down the project understanding what it's for. And also agreeing a way of working with your team so creating a culture in the project and that's a hard process.

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MLOps Coffee Sessions #128 with Simon Thompson, Managing Machine Learning Projects, co-hosted by Abi Aryan.

// Abstract
It's a cliche to say that choosing and running the algorithms is only a small part of a typical ML project, but despite that, it's true! Setting up and organizing the project, dealing with the data asset, getting to the heart of the business problem, assessing and choosing the models, and integrating them with the business processes in production are all at least as time-consuming and important.
Simon has written a book that talks about how these different activities need to be orchestrated and executed, and he hopes that it might be useful for people who are starting out managing ML projects, and help them avoid some of the crunches and catches that seem to trip people up.

// Bio
Simon has been building and running ML projects since 1994 (when he started his Ph.D. in Machine Learning). His first commercial project was for the Royal Navy, and since then, he has worked in Telecom, Defense, Consultancy, Manufacturing, and Finance. This means Simon has experienced a wide range of working environments and different types of projects. As well as working in a variety of commercial environments, Simon collaborated on EU research projects, UK Government-funded research projects, and worked as an industrial rep on three MIT consortia (BigData@CSAIL, Systems That Learn, and the CISR Data Research Board).

Simon was also an industrial fellow at the Alan Turing Institute for a year. This means that he has also seen a lot of the communities' practices and concerns as they developed, and he had the chance to put them into use in a commercial environment.
Right now, Simon is working for a technology consultancy called GFT, and his job there is primarily to deliver ML projects for companies in the capital markets, such as investment banks, although we also do work in retail banking, insurance, and manufacturing.

// MLOps Jobs board

jobs.mlops.community

// MLOps Swag/Merch
https://mlops-community.myshopify.com/

// Related Links
https://medium.com/@sgt101
Managing Machine Learning Projects: From design to deployment, by Simon Thompson:
https://www.manning.com/books/managing-machine-learning-projects
MLOps Community Newsletter: https://airtable.com/shrx9X19pGTWa7U3Y
Language processing. Simon Thompson CO545 Lecture 10: https://docplayer.net/211236676-Language-processing-simon-thompson-co545-lecture-10.html

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Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Abi on LinkedIn: https://www.linkedin.com/in/abiaryan/
Connect with Simon on LinkedIn: https://www.linkedin.com/in/simon-thompson-025a7/

Timestamps:

[00:00] Simon's preferred coffee

[00:35] Introduction to co-host Abi Aryan

[01:14] Introduction to Simon Thompson

[01:21] Takeaways

[04:21] Simon's background

[04:54] Subscribe to our Newsletter and join our Slack channel!

[05:40] Managing Machine Learning Projects: From design to deployment book

[07:40] Simon's inclination toward computers and engineering

[08:59] Simon's first computer project

[10:05] Simon's plan for computers and engineering