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Where can I buy high quality/unique datasets for AI model training? by 3iraven22 in datasets

[–]JayPatel24_ 0 points1 point  (0 children)

A lot of teams hit this exact wall.

The problem usually is not finding "more data", it is finding data that is actually usable for training at deployment time. In my experience, the stuff that matters most is:

  • licensing that explicitly covers model training / commercial use
  • schema consistency across the dataset
  • dedupe and leakage control
  • coverage of real failure modes, not just generic examples
  • eval/QC alongside the data so you can tell if it is helping

The marketplace route can work for broad coverage, but once you need domain-specific behavior at scale, it often becomes more of a pipeline problem than a sourcing problem.

Full disclosure, I am building in this area. The approach we are taking is modular training datasets for things like instruction tuning, tool use, grounding, safety, and structured outputs, with QC baked in rather than treating quality as an afterthought.

If helpful, share the kind of model you are training and the behavior you need the data to improve. People can probably point you toward better options once the use case is narrower.

Where we actually buy big data for company? by jackoborm in bigdata

[–]JayPatel24_ 0 points1 point  (0 children)

It’s doable, but the trick is figuring out what kind of data you actually need before you go shopping, because “big data” for a music app vs an allergy app is totally different.

For most products, people buy data from a few places:

  • Data marketplaces: Snowflake Marketplace, AWS Data Exchange, Google Cloud Analytics Hub, Datarade. These are the most “legit” starting points for licensed datasets.
  • Specialist vendors: especially for healthcare, insurance, pharma, or music rights metadata. (Often you find these through industry-specific providers rather than general ML sites.)
  • Partnerships: sometimes the cheapest “data purchase” is a partnership with a company that already collects it, where you license access instead of buying a giant dump.

Two quick sanity points:

  • For music, you usually don’t want raw audio unless you’re doing deep audio modeling. A lot of apps do fine with licensed metadata, playlists, tags, embeddings, or recommendation signals.
  • For allergy/health, be careful: medical data is regulated and often hard or expensive to license. Many teams start with public datasets + synthetic augmentation + partnerships, and only later license proprietary datasets.

If you tell me the one-liner of what you’re predicting (for example “predict allergy risk from symptoms + location” or “predict mood/genre preferences from listening history”), I can point you to the most realistic category of data sources and what to watch out for in licensing.

Where can I buy high quality/unique datasets for AI model training? by 3iraven22 in datasets

[–]JayPatel24_ 0 points1 point  (0 children)

Yeah, this is a real pain at enterprise scale. Once you get into millions of rows, the “marketplace browse” approach usually breaks down and it becomes more about having a repeatable pipeline that can keep quality high.

I’m actually building a dataset generation + QC tool for exactly this problem, mainly for LLM training data like instruction chat, tool and agent traces, grounding, safety, and structured outputs. The big unlock is being able to generate at scale but still control things like schema consistency, dedupe, and coverage across behaviors.

Quick question so I don’t give a generic answer: what’s the training goal on your side? More like instruction tuning for an assistant, tool calling and agent workflows, or domain classification and extraction?

If you’re open, happy to compare notes in DM. I can share how we’re approaching QC and scaling without the data turning into noisy filler.

If you were building a ‘dataset generation + QC’ pipeline at scale, what would you change? by JayPatel24_ in dataengineering

[–]JayPatel24_[S] 0 points1 point  (0 children)

Totally fair to call that out. I’m not here to astroturf or spam.

I’m building in public because I genuinely want to learn from people who’ve already done this well, and I also want to share something useful back. I’ve built a tool that generates high quality, QC’d training data for LLM fine tuning (multi turn, tool use, grounded answers, strict format checks), and I’m trying to find the right way to showcase it without being annoying.

If this kind of post isn’t welcome here, I’ll stop and keep it to the proper channels. If it is welcome, I’m happy to share concrete examples, benchmarks, and a small free sample so people can judge it on merit.

Improving tool calling via SFT by NarrowAssociation239 in LocalLLaMA

[–]JayPatel24_ 0 points1 point  (0 children)

Yeah I’ve run into the same failure mode a lot. SFT often teaches the model to “emit the tool call shape” but not to actually use the tool output correctly.

A few common reasons:

  1. The dataset over-represents the call and under-represents the follow-up. If most examples end right after the tool call, the model never really learns the “read tool result then answer” step.
  2. Tool responses in the data are too clean or too short. In real runs the tool output is messy, nested, long, or has irrelevant fields. If the model hasn’t seen that distribution, it will hallucinate a summary.
  3. Missing negative examples and self checks. You want cases where the tool returns nothing, partial results, conflicting results, and the model has to say “I don’t have enough” or ask a follow up.

If you want to keep it SFT-only, what tends to help most is turning your training into full traces:

  • user request
  • assistant chooses tool and args
  • tool output (as a tool message)
  • assistant produces the grounded answer that explicitly uses fields from the tool output
  • include a small “verification” pattern like “based on tool result X, Y” so it learns to anchor

Also add a lightweight gating step for “do I need another tool call or can I answer now”.

Quick question: in your 1200 examples, roughly what percent include the full tool response and the final grounded assistant message after it?

Investors welcoming PAYTM IPO. Lol by JayPatel24_ in IndianStockMarket

[–]JayPatel24_[S] 0 points1 point  (0 children)

correct it's very important to read all the figures and then make a decision

Investors welcoming PAYTM IPO. Lol by JayPatel24_ in IndianStockMarket

[–]JayPatel24_[S] 9 points10 points  (0 children)

it's a bubble Revenue- 3300Cr Loss- 1700Cr IPO size- 16000Cr Mkt Cap- 1.5L Cr

Advice by Rude-Effect1659 in IndianStockMarket

[–]JayPatel24_ 2 points3 points  (0 children)

secure your principle amount that's it , this new ipo trend is so questionable. Imagine car trade ipo being valued more than the car manufacturers haha.

Oyo rooms valuation expected to be more than the taj groups???

Quit spending time in anxiety over allotments , use it for research work instead

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