41–50 of 64 posts
Not I, although I tried. All the ML engineers I have encountered thus far have a Ph.D in physics or math. No way I can compete with that level of education!
It's not the usual path, but it's not impossible either.
Earlier quoted context omitted.
It's about the same tier as BI dashboards. Huge breadths of data you don't produce but are nevertheless accountable for. All your stakeholders can easily stream-of-consciousness rattle off dozens of new metric/feature ideas or questions about the feature/metric values they're seeing over the course of 45 seconds, each one taking you tens of hours eyeballs deep in SQL to even begin to answer.
A job we would be glad to have replace by an AI for sure
Earlier quoted context omitted.
>90% of the work in these teams is not core ML and is more mundane work supporting these models, such as data piping, cleaning, feature generation, experimentation, and real-time serving. You'll get plenty of experience in working directly with ML systems. MLOps is what you're describing, and it's probably the number one field I'd recommend someone to go down right now as a backend dev.
This is the #1 thing VC backed startups are trying to automate away
In short, all this AI/ML stuff is just buzzword, ultimately the work you will do in almost all these companies is regular run of the mill work nowhere related to ML or AI
Anything and everything is AI/ML today. A few years ago we used to have a product which would keep track of inventory on display shelf (basically how many items are on shelf) and inventory in backroom and based on that math would generate an alert to the store owner if the number of items in backroom were lesser than a threshold. Basic math. That same product is being sold today without a single line of code change a…
I am in the process of making this transition now. I joined Grab.com on their Safety team and started working on their face recognition technologies. This got my feet wet in ML. Now I am leading their content moderation efforts. TL;DR: Find an "ML adjacent" engineering role and take on ML/AI work. "ML adjacent" roles could be, content moderation, safety, ads, and search.
When did you join and how is it going? I had an offer from them ~4 years ago and this was one of the teams that I was in talks with.
I’m happy with the work I do. The Company culture requires some thoughtful navigation. I’m also happy with the benefits (like travel and working from asian).
Earlier quoted context omitted.
Why is that your number one recommendation?
because 90% of the industry work is MLOps the pipeline usually goes 1. make a POC inside a Jupyter Notebook with some scrappy, data and off-the-shelf model, define metrics and train a baseline to see if the whole ML endeavour might even be worth it 2. Do error analysis, find better data, tune parameters, re-train to see if you can improve upon the baseline 3. Make the first deployment, setup data collection 4. Automa…
Not exactly AI/ML, but I had some years of experience in Android, then RoR and engineering management. Then got an MS in Bioinformatics and now do PhD in Medicine. Which is really data analysis of sequencing data. I use some “AI” models as well. I’m doing a PhD at a company, away from the academic institution where I’m formally enrolled. So it’s kind of like a regular job. But I’ve also seen colleagues pivot into dat…
Quit my job in Fintech to work on my own AI startup. I built https://FakeYou.com as a side project, and it blew up. I quit my job after I realized the potential, added monetization, and started to broaden what we do. I've been working on https://storyteller.ai for a year and plan to launch our platform soon. Both of these tool sets reinforce one another. I'm hiring folks that were engineers that want to do AI instead…