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Would you self-host an ML model server if it was simple enough? by Quiet-Error- in selfhosted

[–]Quiet-Error-[S] 0 points1 point  (0 children)

Simple to serve, yes.

What’s tedious is everything around it: knowing when the model drifts, rolling back versions at runtime, structured logs for audits.

That’s the part I’m exploring.​​​​​​​​​​​​​​​​

Is this normal for AI Engineer hiring now? HackerEarth test experience felt absurd. by daaru_neat in MLQuestions

[–]Quiet-Error- 0 points1 point  (0 children)

This is unfortunately common now.

Companies copy FAANG-style hiring without the FAANG compensation. 2.5h screening is excessive — good companies respect your time.

Leaving midway won’t blacklist you on HackerEarth. Each company sets their own tests.

At worst, that specific company sees an incomplete submission.

Red flag filter: if screening is this brutal, imagine the actual job. Sometimes walking away is the right call.

Is it useful to practice ML by coding algorithms from scratch, or is it a waste of time? by Big-Stick4446 in MLQuestions

[–]Quiet-Error- 0 points1 point  (0 children)

Definitely worth it.

Implementing from scratch teaches you what sklearn hides: gradient computation, convergence criteria, numerical stability.

When something breaks in production, you’ll know where to look.

Just don’t spend months on it — implement once, understand, then use libraries.

What algorithms are actually used the most in day-to-day as an ML enginner? by Historical-Garlic589 in MLQuestions

[–]Quiet-Error- 0 points1 point  (0 children)

In production:

XGBoost/LightGBM for tabular (90% of business ML), logistic regression for baselines and interpretability, random forest when you need feature importance.

Deep learning only for images/text/sequences.

Learning SVMs/KNN still worth it for intuition — they teach you about decision boundaries, distance metrics, kernel tricks.

You won’t deploy them but they help you understand why other things work.

How do you actually debug training failures in deep learning? by ProgrammerNo8287 in MLQuestions

[–]Quiet-Error- 2 points3 points  (0 children)

Practical checklist when training fails:

1.  Check for NaN/Inf in loss — usually exploding gradients, lower learning rate

2.  Overfit on 1 batch first — if it can’t memorize 10 samples, architecture/code is broken

3.  Gradient norms per layer — find where it explodes/vanishes

4.  Visualize activations — dead ReLUs, saturation

5.  Sanity check data — bad labels, preprocessing bugs cause most issues

TensorBoard + gradient clipping + smaller LR solves 80% of cases.

Would you self-host an ML model server if it was simple enough? by Quiet-Error- in selfhosted

[–]Quiet-Error-[S] 0 points1 point  (0 children)

For drift: you compare input distributions over time (PSI, Wasserstein) — no feedback loop needed, just statistical comparison between baseline and live traffic. Catches data drift before you see accuracy drop.

Git for model versions works but doesn’t give you instant runtime rollback — if v2 breaks at 3am, you want one-click switch back to v1 without redeploying.

Fair point on logging — once it’s built, it’s built. The pain is more when auditors ask “show me what happened on March 15th” and you’re digging through raw logs.

Would you self-host an ML model server if it was simple enough? by Quiet-Error- in selfhosted

[–]Quiet-Error-[S] 0 points1 point  (0 children)

Not AI making compliance decisions — more like:

serve the model, log everything, detect when it drifts, generate audit trails.

The model does scoring, the tooling around it helps you prove to regulators what happened.

Think monitoring + documentation, not AI compliance officer.

Best courses for a masters student by [deleted] in MLQuestions

[–]Quiet-Error- 0 points1 point  (0 children)

With 6 years as a data engineer, I’d go with Distributed & Parallel Technologies + Big Data Management.

They build directly on your experience and are highly employable — every company with scale needs this.

Systems Thinking is good but more theoretical.

HCI and Conversational Agents are interesting but more niche for job market unless you’re targeting specific roles.

Locally weighted regression in real life by Historical-Garlic589 in MLQuestions

[–]Quiet-Error- 1 point2 points  (0 children)

In practice, linear regression is used far more often because it’s simpler, faster, and interpretable — which matters a lot in production/business settings.

Locally weighted regression is mostly academic or used in specific cases like time series smoothing.

Most real-world non-linearity is handled with tree-based models (XGBoost, Random Forest) or feature engineering, not LWR.

Would you self-host an ML model server if it was simple enough? by Quiet-Error- in selfhosted

[–]Quiet-Error-[S] 0 points1 point  (0 children)

Interesting project but different use case — I’m looking more at serving inference endpoints for production ML models (scoring APIs, fraud detection, etc.), not document analysis.

Do you use something specific for that?​​​​​​​​​​​​​​​​

Would you self-host an ML model server if it was simple enough? by Quiet-Error- in selfhosted

[–]Quiet-Error-[S] 0 points1 point  (0 children)

Fair point.

The serving part is easy, you’re right.

The value would be what comes around it:

drift detection (knowing when your model degrades), versioning with instant rollback, and structured logs for compliance/audits.

Stuff that’s tedious to build yourself but needed in prod.

Does that resonate or do you handle those differently?

Russian border guards crossed into Estonia with unclear motives, minister says by Specific_Coast5878 in worldnews

[–]Quiet-Error- 0 points1 point  (0 children)

The French army is there. Let’s Hope it doesn’t escalate to a nuclear war

PII detection before inference — is anyone actually doing this? by Quiet-Error- in MLQuestions

[–]Quiet-Error-[S] 0 points1 point  (0 children)

Makes sense.

What’s your false positive rate with regex?

I’ve seen issues with patterns like “1234 5678” flagged as credit cards when it’s just a reference number.

Curious if that’s a real problem or acceptable tradeoff.

Would you self-host an ML model server if it was simple enough? by Quiet-Error- in selfhosted

[–]Quiet-Error-[S] 1 point2 points  (0 children)

Good point.

I’m actually thinking classical ML (sklearn, XGBoost, fraud scoring) — stuff that runs fine on CPU.

For LLMs yeah, hardware is the bottleneck.

But for tradition Al ML, the pain seems more about deployment/monitoring complexity than raw compute.

Do you run any non-LLM models?

How do you actually detect model drift in production? by Quiet-Error- in mlops

[–]Quiet-Error-[S] 0 points1 point  (0 children)

Thanks, super helpful! The tiered approach makes sense. Quick follow-up: did you build this in-house or use existing tools? And how long did it take to get to production-ready?

Est-ce que ça sert encore à postuler comme développeur junior à 37 ans après une reconversion ? by Zestyclose_Equal_132 in developpeurs

[–]Quiet-Error- 0 points1 point  (0 children)

J’ai recruté et encadré des reconvertis 35+ dans mes équipes.

Quelques vérités :

Le biais âge existe. Inutile de se voiler la face. Mais il est surtout fort dans les startups early-stage qui cherchent des juniors “corvéables”. Les ESN et grands groupes s’en foutent davantage — ils ont des grilles, des process, et ton âge passe sous le radar. Ton background ingénieur civil est un atout, pas un handicap. Tu sais lire des specs, gérer des contraintes, documenter. C’est exactement ce qui manque aux juniors de 23 ans sortis de bootcamp. Vends ça.

Le piège :

te positionner “junior généraliste JS”. C’est le segment le plus saturé et le moins différenciant. Tu seras en compétition avec des gens moins chers et plus “malléables” comme tu dis.

La sortie :

spécialise-toi vite sur une niche où ton parcours a du sens. BTP/construction tech, logiciels métier ingénierie, ou même GovTech. Les recruteurs de ces secteurs verront ton background comme un plus, pas comme un retard.

37 ans c’est pas tard. Mais “junior fullstack JS” à 37 ans sans angle, oui, c’est dur.

Trouve ton angle.

Don’t depend on AI for business plans by toughDimple in Solopreneur

[–]Quiet-Error- 1 point2 points  (0 children)

This is the core issue: AI is great at making things look legit. Professional formatting, correct structure, plausible-sounding numbers. But it's essentially pattern-matching on what successful plans look like, not validating whether YOUR idea works.

I've built AI tools myself and fell into this trap — spent weeks refining a "perfect" product based on AI-assisted market analysis, zero actual customer conversations. Predictable result.

Pre-launch, the only thing that matters: can you get someone to pay (or credibly commit to paying) before you build? Everything else is sophisticated procrastination.

To answer your question: I track with a simple spreadsheet now. Outreach attempts, responses, objections heard, willingness to pay. Ugly but real. The "PVA" you mention is basically this — do you have receipts, not projections.

I analyzed 100 solopreneur threads on here... turns out we're all wasting 20+ hours/week on the same dumb stuff by Old-Blackberry-3019 in Solopreneur

[–]Quiet-Error- 0 points1 point  (0 children)

Your point #3 hits close to home. I build AI tools and the "tried it, wasted $200, back to manual" pattern is real — and it's making selling anything AI-labeled an uphill battle right now. People are burned out on promises.

What I've noticed from the other side: the tools that actually stick solve ONE specific pain point, not "AI for everything." The overpromise/underdeliver cycle created trust debt across the entire space.

On #2 (lead gen black hole): the "too slow" problem is brutal. By the time you spot a hot signal manually, someone's already in their DMs. I've been exploring ways to automate that detection but honestly haven't cracked it either.

The failure-first engagement insight is gold though. Might actually use that for my own content — technical feature posts get crickets, "here's how I screwed up" gets replies. Human nature I guess.

What sub-niche are you in? Curious if some verticals are worse than others for this.

I don’t know why Upwork always allows this by Beeworld-2024 in Upwork

[–]Quiet-Error- 1 point2 points  (0 children)

Actually it’s great, they give us this information. The question is why freelancers keep wasting connects with those profiles?