Proxies for AI, Machine Learning and Dataset Pipelines
Research crawls, enrichment jobs, and evaluation sets often need public web access from more than one location. Use proxies for lawful public sources, log geo and session mode, and keep training data free of secrets you should not have.
What AI and ML public-web pipelines need
ML teams fetch public pages for enrichment, evaluation, and sometimes pre-training research. The fetch layer should be as documented as the tokenizer. If every request exits from one lab subnet, your 'web' data is really one building's web. Proxies add labeled geography and session control. Sticky sessions help multi-page public walks used in evaluation. Rotating sessions help independent URL enrichment. Neither one is a license to ignore robots, copyright, or privacy law.
- Geo-labeled public fetches for datasheets
- Sticky evaluation walks versus rotating enrichment
- Separate IPs for train-adjacent versus eval jobs
- HTTP and SOCKS5 for mixed research stacks
Proxy types that fit this use case
How Proxy Grove supports this workflow
Do not build datasets from authenticated content you do not have rights to, from personal data you cannot process, or from sources that forbid automated access. A proxy will not make that ethical or legal.
Proxy Grove offers Mobile, Residential, and Corporate Proxy with HTTP/SOCKS5. Unlimited per-IP plans start from $2/IP/day for Residential and Corporate, and $4.50/IP/day for Mobile. The network lists 246 countries; city targeting is available where supported.
Availability can vary by product. Put that caveat in the datasheet so a reviewer does not think you sampled a city you never targeted.
Residential Proxy is the common path for public consumer pages used in enrichment. Unlimited IPs from $2/day keep a research cluster from turning into a bandwidth negotiation.
Corporate Proxy can suit academic and enterprise sites that already expect office networks, plus internal eval harnesses that talk to public documentation.
Mobile Proxy is for experiments that explicitly need a 4G/5G view of a public page. At $4.50/IP/day it is a treatment in the experiment design, not a free upgrade.
The automation guide helps ML ops treat proxies like any other cluster dependency. Documentation has the connection details to drop into a secrets store.
Data collection and web scraping pages cover adjacent governance. This page focuses on dataset cards, eval isolation, and not poisoning a model with unlabeled geo mix.
Common workflows
Enrichment joins
Fetch public pages for IDs you already have a right to process. Store the geo and product of the fetch next to the feature.
Evaluation corpora
Build a frozen public URL set and refetch through sticky country endpoints so eval is comparable over time.
Localization tests
Compare model outputs against public local pages from the target country. Do not mix countries in one eval slice.
Canary domains
Reserve a small public set to detect block pages before they enter a training or eval mix.
Eval versus crawl IPs
Keep high-volume research crawls on different IPs from low-volume human eval tools.
Datasheet updates
When you add a country or switch Mobile versus Residential, version the dataset card the same day.
Which proxy type to choose — and why
ML fetch layers should not silently mix network types. Residential is the usual public-web default, Mobile only when the research question is a carrier path, Corporate for stable eval tools. Tag every shard. Mixing unlabeled Mobile into a Residential corpus is a confound, not extra diversity.
Mobile Proxy
Use when the research question is the phone-network rendering of a public page. From $4.50/IP/day. Mixing it unlabeled into a Residential corpus invalidates the experiment.
Residential Proxy
Default for public consumer web used in enrichment and eval. Sticky for multi-page tasks, rotating for independent URLs. From $2/IP/day, unlimited per IP.
Corporate Proxy
Fits public institutional sources and office-like eval harnesses. Test before using it as a stand-in for consumer web.
Why location changes what you see
Treat country as an experimental factor. A model that looks strong on a mixed-geo scrape may fail in the market you ship to.
City targeting is available where supported. Enable it only if the task is metro-specific, and document whether it was on.
Do not claim 246-country training coverage if you only enabled a handful of Locations, or if a product was unavailable in a region.
A practical workflow
- Write data rights and exclusion lists before any cluster job.
- Prefer licensed datasets and APIs; use public HTML only when allowed.
- Choose Mobile, Residential, or Corporate as an experimental factor.
- Put HTTP or SOCKS5 endpoints in the job's secret store.
- Set sticky versus rotating in the job spec.
- Tag parquet or JSONL with country, product, and session mode.
- Run canaries; drop partitions that look like block pages.
- Version the datasheet whenever geo or product mix changes.
Practices that keep the work trustworthy
- Keep eval IPs and exploratory crawl IPs separate.
- Strip credentials, cookies, and personal data in a sanitizer.
- Honor robots and copyright; cite sources in the datasheet.
- Cache public pages used in eval so you are not refetching the live web during a paper deadline.
- Review Locations when expanding language coverage.
- Do not use captcha-solving as a substitute for permission.
- Record user-agent alongside proxy product.
- Read the automation guide so research jobs have owners and off switches.
Common mistakes
- Training on authenticated or paywalled content you do not have rights to.
- Omitting geo from the dataset card.
- Rotating identity in the middle of a multi-page eval task.
- Calling Mobile data 'higher quality' without a hypothesis.
- Letting a blocked IP silently fill eval with interstitial HTML.
- Claiming city-level localization when targeting was not supported.
- Publishing proxy passwords in a research repo.
Why teams use Proxy Grove for this work
Model pipelines need egress that can be cited: Residential, Mobile, or Corporate, HTTP/SOCKS5, sticky or rotating, country on the record. Proxy Grove exposes those as product settings rather than a lab NAT nobody documented.
Unlimited per-IP plans from $2/IP/day (Residential and Corporate) or $4.50/IP/day (Mobile) fit research clusters that run enrichment every day without a surprise bandwidth SKU.
Documentation, the automation guide, and Locations (246 countries listed, availability by product, city where supported) belong in the same folder as the dataset card.
Attach collectors and evaluators to Residential, Mobile, or Corporate endpoints. Record country, product, and session mode in the dataset card.
Give model pipelines egress you can cite in a datasheet
Attach collectors and evaluators to Residential, Mobile, or Corporate endpoints. Record country, product, and session mode in the dataset card.
AI and ML proxy questions
No. Collect only public sources you have a right to use, at a polite rate, in line with terms and law. A proxy is egress, not a corpus license.
Mobile, Residential, and Corporate are different network paths. Unlabeled mix is a hidden confound in eval and enrichment.
If the task walks multiple public pages as one unit, yes. If each URL is an independent feature, rotating across IPs is fine.
Not for the API itself. You need proxies when your own collectors fetch public web pages as features or eval context.
Exclude sources, strip fields, and review samples. Proxies do not anonymize HTML. Follow your privacy review.
You can place jobs in countries we list, subject to product availability and source terms. Confirm Locations; do not assume every city is targetable.
Residential and Corporate from $2/IP/day, Mobile from $4.50/IP/day, unlimited usage on the IP. Isolate eval from bulk research IPs.
Documentation for connection syntax, the automation guide for operations, and the dataset card for geo and product mix.