Open Source Q&A and Videos

I'm trying to break into AI as a Machine Learning Engineer. I want to demonstrate expertise in AI/ML topics and capture a "Wow" factor by contributing to a well-known open source AI project.

I'm looking for suggestions spanning CV, NLP, tabular data and collaborative filtering -- not excluding but not limited to the latest GenAI stuff.

Some context: I have an MS in CS, did CV research, and completed Jeremy Howard's ".

The itself is a popular target for a first contribution. See:

2. (if the link doesn't work, it's also discoverable: search "fast.ai"). #fastai-dev is the contributors' channel.

One concern is identifying something that's non-trivial but tractable for a relative newbie. I realize there's tension between this and achieving that "Wow" factor at least in the beginning. I'm not sure a contribution to, say, PyTorch or scikit-learn is achievable from where I stand, but I could be wrong. Perspective on how to spot/scope opportunities would be appreciated too.

Thanks very much!

Potentially relevant questions on Taro:

I have never contributed to open source but would like to start doing so. I believe this will make a huge difference in landing big tech jobs. Would like some references, steps, directions for beginners to get started in this journey.

@Alex Earlier you mentioned:

Big Tech open-source is obviously ideal (many engineers got hired at :meta: contributed to their OSS), but it's extremely hard to ramp up on the library and get a major PR landed. And it's not like 1 PR will do it - You'll probably need 5 to 10 to get the attention of hiring managers. However, like with most things, it will get much easier over time (the first one's the hardest).

And here: , you said:

These companies all have a large OSS footprint, and I know several people who got hired into these companies by submitting major PRs into them.

What are the parameters for determining whether something is a major PR in Big Tech Open Source according to you? Am currently working on Microsoft Windows Terminal.

Would you consider the below major PR candidates that would get the attention of hiring managers?

Btw they're both from this list as heroic tasks (7158 on completed tab since I've already completed that one):

To my understanding Major PR means lots of people want it and 7158 has around 100 upvotes so that's Major PR. But there are also some PR's where people make commits with 500 - 1000 lines of code

TL;DR Contribute to Open Source ML or do side projects for ML. Which do you suggest is the better option?

I just started a new job, but due to circumstances (visa, tough market), I had to take the first job I could take and I ended up in a devops/production support role where I cant really write much code or write any production code (literally dont have access to dev code). I dont plan to stay here long (>6 months).

I read the infamous "" post and wanted to do side projects so that I am not rusty

Context on me: 80% of my background is in Applied ML/Data Science and 20% is software engineering. I am interested in pursuing as an ML Engineer/Data Scientist

Open Source

Pros

• Tons of open source ML stuff supported by big tech companies
  • Meta has a ton of OS projects
• Huggingface is open source
  • Lot of companies use ML models from huggingface (for e.g. BERT for NLP). Would contributing to this on huggingface be seen as impressive?
• Exposure to working on large codebases, good software engineering practice as well

Cons

• Minimal Impact
• Hard to showcase my achievements, especially on LinkedIn

Projects

Pros

• Ability to make and measure impact
• easy to showcase
• learn a lot

Cons

• For ML, projects with impact is hard to do. Most ML applications is based on improving current products using existing data
• Experience from building ML projects might not translate to what I would do on the job as a lot of it involves working with Engineering around data
• It takes time and a lot of effort to have a ton of downloads
• Can end up taking a lot of non-ML work work (web design/frontend) which is not relevant to MLE

Final question: If I were to do open source, what is the best way to showcase on LinkedIn?

• Do you suggest adding the company you did OS for under the experience section and saying "Open Source Contributor"? My concern with this is that it may sound scammy/shady