GLP-1 and GIP are both hormones the human body makes in the gut. The famous drugs are mimicking those hormones. This is more akin to taking supplemental testosterone than it is to taking Fen-Phen or whatever.
You may want to look into https://exist.io/. It's a very indie developer duo out of Australia (IIRC). And also IIRC they were looking for a buyer on Twitter some time ago.
I'm an ML Engineer who's really closer to an MLOps role. I'm weak on ML and strong on data, scaling, cloud stuff, infra as code, making processes not suck, kinda everything _but_ the ML. So take my opinions for what they are worth, and keep in mind that the role of ML Engineering at company A != ML Engineering at company B.
I've described ML Engineering as putting the "science" in Data Science because we help introduce reproducibility. For example, I can take your model training and make it a robust process that happens over a huge amount of data on a daily basis with all the monitoring, logging, and reliability stuff surrounding that.
Some topics I would personally want to see for an ML Engineer on my team (and again, "ML Engineer" has less of a solid definition across the industry than "frontend engineer" or other roles that have been around longer)
- Docker: can you containerize your code? Can you interact with a local container?
- Model serving: at a basic level, can you wrap an API around a model? There's lots more systems design stuff here if you want to go deeper on model serving platforms.
- CI/CD: do you know what Jenkins does? (Or equivalent) Can you talk about a coherent code testing strategy for ML code? How would you deploy a model service using a system like Jenkins?
- Cloud stuff: you don't need to be an expert, but can you interact with cloud APIs directly or through Terraform, spin up instances, know the difference between object storage and databases, and do you have some Kubernetes experience (run a pod, get the logs, take some debugging steps when something's wrong).
- Modern MLOps: model registry systems like MLFlow, feature stores (DIY preferred but vendors ok)
- Scheduling and Pipelining: Airflow, Vertex Pipelines, lots of options here but those are the biggies. Know how to use these for basic data pipelines, model training, service deployment, and why and how you can deploy these via CD
- Monitoring: know the difference and have strategies around monitoring systems metrics (cpu usage, etc) and model metrics (data drift, etc)
A lot of this stuff is harder to learn on your own because it often comes up in the context of larger teams and enterprise scale, where monitoring and reliability turn into KPIs that execs look at, but this is, to me, the stuff that defines the difference between a Data Scientists and an ML Engineer.
Changing duvet covers! I learned the Burrito Method [1] and never looked back. When I send this to people, the reactions vary from "OMG WHAT YOU CHANGED MY LIFE" to "you srsly didn't know that?"
In some ways I feel lucky that tech was an early career change for me. I was trying to get a career in the arts off the ground, and it was going poorly. I was a recent grad millennial in the economy of the 2008 recession.
The decision to turn my back on what I thought was my passion was a profound spiritual experience. The decision to change came from outside of me. The decision of what path to follow was up to me though.
Tech was hiring and hiring like crazy, and I wasn't going to do an unprofitable degree twice so CS it was. I had a job before I graduated making 4x what my mom was making at her non-profit admin job.
If I hadn't pursued my art career first and had the chance to get deeply disillusioned with it, I would definitely be sitting at my desk trying to write code and thinking "what if... I'm not made for this... there's something else..." The truth is that I'm not cut out for the arts industry. I like stability, I like being salaried, I like having the upper hand in the hiring market (I know Big Tech is doing layoffs, but try spamming applications for a year to everything you can think of until the only place that calls you back is a cashier position at a grocery store. I have skills that are in demand now.) I like work that is decent and stimulating enough but which is definitely not "my passion" because that helps me keep boundaries on it.
I feel for folks who didn't get that chance to try out that other thing, who went straight into this career maybe because they wanted to, maybe because they didn't have the safety net I had that allowed me to do a second degree, maybe because life has held them down and change doesn't feel like an option. I've been out there with my chosen field and gotten burned hard by it so I'm content to stay put. It's definitely one of the cliche sayings about how the lows make the highs much higher.
I have no useful advice for anybody beyond their very early 20s facing this question. I know I would be eaten by this question if I hadn't already gotten my answer at the start.