[AJRF]

I've moved from "traditional" software engineering to a role of working with ML (building + deploying models used in product features) and of the team I work with - and my extended communication with developers at other companies making the same transition - every single person has said the Francis Chollet book (Deep Learning with Python) is all they really needed.

It walks a very thin line between too little info and *just* enough to get you to the point where you know what you don't know (the productive point) and it explains the Math in code samples. It really is a very good way of teaching. When I was reading, I thought the theory covered was too far from the Mathematical base, but I found my self being surprised at how I could hold my own in discussions that moved in to theory.

That said, this likely won't be enough for you to be a researcher - but I imagine for a lot of people tempted by courses like the OP - that isn't the actual end goal anyway.

[ra7]

Thanks for the book recommendation! I’m interested in making the same transition. Can I ask what you did to be considered for a role in ML coming from a software engineering background? Did you showcase any personal projects in your resume?

[AJRF]

So this might not work for you, but I will tell you my path anyway.

1. Was one of the first members of the #ai slack channel inviting some people I had in person conversations about AI with.

2. I posted _a lot_ in there. Stuff about regulatory updates, people using co-pilot, cool github repos, little demo projects I was working on.

3. Now this was pure luck and probably the best thing to push me over the boundary, there was a hackathon. I thought "Hmm if I make a kick ass demo showcasing generative AI here, a lot of high up people will see it" - that 100% happened, CTO reached out to me saying demo was great and that people will be in touch.

4. I started really digging in to how I could provide value to our existing data team - be that code, deploying things, bringing some of my engineering know how to that team. This point the #ai channel really started to grow and the head of data and engineering started talking to me and directing people my way based on what they saw at the hackathon.

5. Did a demo of my hack in the company all hands which the CEO was MC'ing.

6. Started having fortnightly 1 to 1s with head of data at this point

7. Floated idea of team taking a little subset of good and motivated people from other teams for a short time to investigate and implement LLMs in some small way into our apps. That team has now grown to effectively investigate any and all use cases (internal and external) for generative AI.

8. I started reading more theory and also following a bit of a road map for things I should learn to have a better picture of how to actually bring LLMs in some form to production (fine-tuning, vector dbs, functions, guard rails).

9. Now I am just building some quick feature in the mobile app to show case the value of the team to exec as quick as I can, which should give us few months cover to work on the thing I am really interested in - multi-arm bandit LLM that uses our existing models.

This was pretty much it. Seems trivial, but in between each points was lots of reading, tinkering, working on weekends, but its totally possible. The ML + AI focused PhD's in your company likely need help from engineering but don't know it - bringing those two groups together quickly shows how you can be useful.

This post was helpful; https://blog.gregbrockman.com/how-i-became-a-machine-learnin...

[ra7]

Great response, thank you!