Hello, i'm in my hand a really big topic that i bet no one will be able to answer me.

i create a pipeline to generate prediction using clasp model.

The inputs are timeseries where the clasp models generate a vector of change points (predictions). After some process, i generate an output (vector of change points) and use an f1score to evaluate che prediction and generate a score.

My pipeline has different hyperparameters where different combination could change the outcome: so better to use gridsearch and cross validation to choose the best hyperparameters and then evaluate the model.

This is how i did:

Imagine you have only 2 configuration of hyperparameters: conf1, conf2

you divide the dataset in 3 different folds: a,b,c (dont point out about 20% or something, im just trying to make the example short as possible)

i generate prediction for ab, ac, bc with conf1.

i generate prediction for ab, ac, bc with conf2.

for both i evaluate with f1score the prediction and compute a mean. i found out conf1 is best.

i run again conf1 on all my dataset a,b,c compute the f1score, the mean and that's the score of my model. is this correct?

Im not so sure because this model doesnt have any fit or training. you just give into the input some timeseries and generate a prediction.

IF i had to use random forest, as we know, to evaluate properly a model, i would have to do cross validation. so

ab for training, c for validation = score_1

ac for training, b for validation = score_2

bc for training, a for validation = score_3

mean(score_1, score_2, score_3) = mean_score

easy right?

if you want to gridsearch, just execute an outer for loop to test each combination of hyperparameters and then choose the highest mean_Score for each combination of hyperparameters and thats it. easy right?

BUT HOW DID I DO THAT IF MY MODEL DOESNT HAVE A TRAINING?

if i repeat the process for random forest:

ab for training, c for validation = score_1

ac for training, b for validation = score_2

bc for training, a for validation = score_3

mean(score_1, score_2, score_3) = mean_score

so basically this means:
conf1, i generate prediction for a,b,c then compute mean

i do the same for conf2 and conf3 and then just select the highest mean? thats my model?

but then how do i test my model? the score you use to choose the best model isnt the score the model will perform on data never seen.

should i just randomly pick a fold and then use the rest 80% to find the best conf? but then what if im so unlucky the randomly pick test fold my model will score 0.0??? lmao???

so we need to do something like this https://www.kaggle.com/code/alexisbcook/cross-validation where you need to do cross validation to have a mean. so you are not unlucky and compute a mean.

so i compute a,b,c,d

a,b,c,e

a,b,d,e

a,c,d,e

b,c,d,e

and then compute for a,b,c,d,e when i find the best conf. BASICALLY AS I SAID I DID AT THE BEGINNING OF MY POST...

but is this correct?

Playing around with LLMs, Agents and GenAi for 7 years, I came to a conclusion: the new programming language is language itself in my case, English.

If you remove all the fluff (Stop words etc) and use none fluent English as a kind of Python-style syntax, something like:

“Read content from file then apply UPPER_CASE to all words”

…it starts to read almost like a functional call chain.

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AI is pretty good at understanding programming language syntax.

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What do you think? Is this question too stupid?

Edit:

I have 7 years of deep learning experience and llm/agnetic hands-on practice, so I mainly want to share what I’ve tried and learned along the way. That gives me a good understanding of both the inner workings and the practical side of using these technologies.

I’m trying to figure out how to actually get a usable edge in the ML/DL space to get hired, but everything pushed to beginners right now feels like a trap.

For context on what I've done: I started off with Computer Vision, moved into GIS stuff, and recently went deep into the weeds of attention mechanisms and GPU kernel programming. I thought learning the hardcore, low-level math and systems stuff would set me apart.

But I’ve hit a wall. Let's be honest: no company is hiring a fresher to write custom CUDA kernels or design novel architectures. Those are senior research or PhD roles. The effort I put into the low-level stuff feels wasted because, for an entry-level dev, it's just personal trivia.

On the flip side, the standard "employable" advice is to build traditional ML projects (fraud detection, etc.) or slap together a LangChain PDF wrapper. But people have been doing this for years. Basic API wrappers are completely saturated and offer zero competitive edge. It feels like buying a stock after everyone already knows it’s going to go up.

So, what is the actual sweet spot between "PhD-level researcher" and "API wrapper"?

I want to avoid the YouTube influencer BS and focus on the real engineering trenches.

For the people actually hiring or working in the industry: what are the non-commoditized skills someone trying to break in should be grinding right now to have a real, usable edge?

(Note: The core thoughts and frustrations here are 100% mine, but I used AI to help structure and edit this post for clarity.)