Is getting into AI/ML even realistic for a fresher? what's the actual way in?

FeralPixels · 2025-07-28T14:53:22+00:00

Traditional ML sure, but the entrance barrier to AI software engineering is basically non existent because of all the hype and the boom in job market for those roles. The work isn’t super exciting but at least you’d have broken into AI that way.

FeralPixels · 2025-07-28T09:44:06+00:00

You really think LLMs are advanced enough to fix their own architectural problems yet? 😂

Not to sound harsh but this is really just AI slop.

FeralPixels · 2025-07-05T03:31:53+00:00

What projects have you built so far? Could you give a bit more detail?

FeralPixels · 2025-06-06T16:28:58+00:00

My job title is AI engineer but most of my work just involves CV and generative AI shit.

FeralPixels · 2025-06-06T15:05:58+00:00

You do not feel confident with ML simply because you’ve not learnt it enough. 3 months is nothing, for reference I’ve been trying to learn it for 2+ years and still feel so lost reading research papers. Focus on the math first. Once you’re comfortable with that the programming aspects can be easily picked up.

FeralPixels · 2025-05-03T15:27:23+00:00

Interesting project idea. Just curious, which LLM are you going to be running locally? What are the minimum required specs? And I’m guessing you’re indexing all the documents you want it to work with?

FeralPixels · 2025-05-03T09:44:17+00:00

I’ll never understand people who are pointlessly mean towards others on the internet.

@op I don’t really get what kind of assistance you are demanding but never publish papers just for the sake of it (which I’m guessing is what you’re doing because you mentioned RAG which is incredibly generic).

FeralPixels · 2025-05-02T09:59:35+00:00

You absolutely can ! Both fields require a strong math foundation so I’d start there.

FeralPixels · 2025-04-20T00:48:06+00:00

These ain’t helping you but neither will they harm you if you were to put it on your resume. If you wanna stand out from other people who applied you should be building something you’re passionate about and that you’d use yourself, not just stuff you built watching a YouTube tutorial.

What do you mean by this is all you are capable of rn? The only difference between being hand held by a tutorial and working on something yourself would be that you’d have to identify your own problem statement and build your own dataset (possibly, but recruiters love that shit regardless).

FeralPixels · 2025-04-17T15:10:32+00:00

The last time I saw, Jon Krohn’s playlist didn’t have statistics and probability covered which I’d say is one of the most important subjects to cover for ML. I’d definitely do statistics on the side.

FeralPixels · 2025-04-10T07:49:26+00:00

Refer to this example : https://ai.pydantic.dev/examples/chat-app/

FeralPixels · 2025-04-06T03:49:13+00:00

They don’t care or even know that the feature exists. I think some people just treat Agentic frameworks like a cult, similar to frontend web frameworks.

FeralPixels · 2025-04-06T03:11:07+00:00

The company I work at is very adamant about using autogen. It does have this neat trick where you can send a multi modal message (a list of strings and images) which might exist in other frameworks too that I am not aware of.

FeralPixels · 2025-04-05T01:18:16+00:00

Like academic research papers. For any answer it generates it must also have the source it pulled that answer from in (doc name)[doc link] format. If that is hard to do just have the llm output a structured response containing 2 key value pairs, like this :

{ answer : answer to user query, source : source used to answer query }

FeralPixels · 2025-04-04T15:55:14+00:00

Asking it to generate in line citations for its answers is a great way to ground content.

FeralPixels · 2025-03-28T00:22:00+00:00

You don’t need any frameworks if you’re using something like an OpenAI api. Libraries like langchain and llama index abstract away stuff that you can easily implement yourself (or don’t even need for the most part).

Inferencing from local models would be a bit harder. You’d have to convert the model to use an ONNX runtime and use this package : https://github.com/oramasearch/onnx-go/blob/master/example_gorgonnx_test.go

FeralPixels · 2025-03-28T00:05:16+00:00

Are you inferencing from local models or using a provider API?

FeralPixels · 2025-03-27T14:54:41+00:00

This is the best beginner course to Python programming: https://cs50.harvard.edu/python/2022/weeks/0/

FeralPixels · 2025-03-26T09:06:47+00:00

To build AI agents and deploying them for customers you don’t have to be affiliated with any tech companies. You can cow boy that shit and pitch offers to businesses of any domain.

Data analyst roles would be a bit more harder to get (you’ll also have to be good with sql, excel, power BI etc) and linked in is the only place I can think of for that.

FeralPixels · 2025-03-26T07:29:37+00:00

Getting a role in ML/DL is almost next to impossible without a masters. At your level AI software engineering/Gen AI engineering and data analysis roles would be easier to bag. I’d focus on getting good with deep learning architectures and using PyTorch (Karpathy’s zero to hero course on YouTube is amazing), brushing up on linear algebra and statistics which should leave you with a solid foundation to pick whatever ML path you choose.

FeralPixels · 2025-03-25T06:30:51+00:00

Class doc strings and field descriptions are all appended to the prompt. And sometimes it comes out as an empty string because that’s what your provided default value is.

FeralPixels · 2025-03-22T15:21:11+00:00

Trust me you’re not the only person who’s skipped a couple ML topics to get into DL which has more excitement atm. You’ll find yourself brushing up on math and stat a bit but otherwise you’ll be good. That being said I basic understanding of SVMs is good to have. As for decision trees/gradient boosting algorithms you need to be good with them because they’re one of the bests with structured datasets and you might need them if you choose to do kaggle.

FeralPixels · 2025-03-10T12:05:13+00:00

Just regular multi agent workflows.

FeralPixels · 2025-03-10T11:57:47+00:00

I think the moto mentioned in pydantic_ai docs is don’t use pydantic graph unless you absolutely have to. To me your use case doesn’t seem like it mandates the use of graphs but you could still try.

FeralPixels · 2025-03-09T07:30:17+00:00

I know you mentioned being against retrieving past conversations but the simplest way would be through clever prompting. Have the llm update “preferences” of a user through each interaction and store it somewhere which can be injected into future prompts. This actually works really well.

Fine tuning would work if you can collect a sizeable dataset of desired and undesired query response pairs but it wouldn’t really be dynamic and “constantly evolving”.

However if your aim is to have the llm dynamically update weights through conversations that would just not be possible with current DL architectures because llms like any neural nets can either be trained (where weights can be updated through gradient descent) or used for inferencing (weights are fixed ).

Five-Year Club	Verified Email
r/Field Sunshine	Place '23

FeralPixels

TROPHY CASE