What is data curation?
Data curation encompasses the entire process of sourcing, cleaning and annotating data. Your model is only as good as your data. Poor data curation can lead to all sorts of problems and they all boil down to one principle: Unintended behavior. Since the dawn of computer science, unintended behavior is something any responsible team or developer should want to avoid — especially in production environments.
In this guide, we’ll cover a variety of tools that can assist you in data curation. From end-to-end enterprise solutions like Bright Data to open source solutions such as Hugging Face and LAION, we’ve got you covered. Once you’re finished reading this piece, you’ll be able to decide which data curation tools best fit your project.
Why should you care about data curation?
If unintended behavior is a major problem in software as a whole, unintended behavior in AI models is also problematic. Bad data creates bad models. Good data creates good models. Understanding data curation will help you prevent difficult model behaviors and unintended bias. We’ve all heard horror stories of poorly trained AI models.
Everybody on your team is responsible for data curation. It starts with your sourcing and ends with your model output. Your team should select relevant data sources — websites, APIs and datasets — that are free from harmful ideas and bias. From there, your data needs to be extracted and given structure. Then, it needs to be cleaned and balanced. Finally, before entering your AI system, your data needs to be enriched using a process called annotation to provide relevant context to AI models.
Best data curation tools
Now that we’ve got an understanding of what data curation is, let’s take a look at some of the best data curation tools and providers around the industry. We’ll cover a host of providers from end-to-end solutions like Bright Data to more niche options used for synthetic data and highly specialized annotation.
Bright Data
Bright Data is one of the biggest names in all of data curation and web data infrastructure. They offer commercial grade pipelines built for data curation at scale. From sourcing, to your AI system and all the steps in between, Bright Data has something to offer.
Here are some of their main products and how they align with data curation.
Sourcing
- Unlocker API: Scrape from even the most difficult sites on the web. If you’ve identified a raw source, the Unlocker API can make it accessible.
- SERP API: Get results from the top search engines on the web: Google, Bing, Yandex, DuckDuckGo, etc. Your team or even AI agents can identify leads quickly using lightweight structured search results.
- Browser API: Run full-fledged remote browsers with proxy integration for stability and persistent session support.
Processing and enrichment
- Scraper API: Extract structured data using pre-built scrapers. Run scrapers on-demand and get clean, ready-to-use datasets whenever they’re needed.
- Datasets: Bright Data offers fully curated historical datasets right out of the box. Plug-and-play directly into your training environment or RAG system.
- Multimodal data: Get annotated LLM-ready data packages even for images and video. This marks a significant expansion past traditional text-based data curation.
- Data annotation: If you’ve got your source but you’re not sure how to prepare it for AI, Bright Data even offers annotation services to help with processing and enrichment.
Appen
Appen has been around since 1996. They offer a more traditional workforce driven approach to data curation. Instead of using automated pipelines and web extraction, Appen uses a human workforce to perform data collection themselves. This positions Appen as an “end-to-end” pipeline of a different sort. For instance, workers collect images from the real world instead of extracting them from the web.
Sourcing
- Remote collection: Workers use their mobile devices to collect real-world data and upload it to Appen.
- On-site collection: Using specialized equipment, Appen allows projects to collect on-site data normally unavailable on the web.
- Device data: Using data collected from devices, Appen collects data usable for AR/VR and autonomous driving.
- Geospatial and location data: Get specialized data for specific geolocations and points of interest.
- Off-the-shelf (OTS): Appen sells premade datasets collected from over the years.
Processing and enrichment
- Data annotation: Appen offers annotation services for text, speech, video and multimodal data sources.
- Supervised fine-tuning: Their workforce will help you supervise and fine-tune your AI model to get the outputs you’re looking for.
- Evaluation and benchmarking: Appen provides evaluation services to identify weak spots so you can improve your model output and training data.
Appen is a legacy AI data company. However, they do provide some unique options that you can’t get with other providers. It’s best for projects that need data collected by people.
Hugging Face
Hugging Face is the leading open source solution for data curation. Hugging Face functions like a GitHub for all things AI. They host AI models and dataset repositories of all kinds. They don’t serve any raw data but they do offer unique solutions for your data curation pipeline.
The offerings from Hugging Face allow you to get free curated data for growing projects with less specific needs.
Processing and enrichment
- Datasets: Immediate access to almost every type of data you can imagine. If you’re looking to get an LLM off the ground, they’ve got exactly what you’re looking for.
- 3D
- Audio
- Document
- Geospatial
- Image
- Tabular
- Text
- Time-series
- Video
- AI models: Open source models on Hugging Face allow you to skip much of the curation pipeline. Pick a pretrained model and fine-tune it from there using curated datasets.
Hugging Face is a free and community-driven project. It’s best for teams that need to springboard their AI development without the demand of a full data curation pipeline.
LAION
LAION is another open source option for teams looking to bootstrap their AI projects. Like Hugging Face, they don’t provide sourcing tools for raw data. LAION provides a handful of highly curated datasets and open source models for experimentation, research and springboarding.
Processing and enrichment
- Datasets: Their core strength lies in their datasets. LAION aims to advance our knowledge of AI by providing these datasets.
- Image/text
- 3D/image/text
- Text/audio
- Models: LAION provides several models available for benchmarking and bootstrapping. DALL-E2 (an open source reimplementation of OpenAI’s DALL-E) and the CLIP models aim to make advanced AI accessible to open source communities.
- Image/text
- Video/text
- Audio/text
- Image/video/audio/text
- Tools: They also provide a variety of processing tools for images such as img2dataset and Clip Retrieval.
LAION is unique in both the commercial and open source worlds. Their offerings, while limited in scope, give your team access to highly-curated datasets, models and tools.
Scale AI
Scale AI is an enterprise option tailored around processing and enrichment. They specialize in annotation, synthetic data and evaluation.
Processing and enrichment
- Annotation: Scale AI positions itself as a best-in-class annotation service. They offer streaming and batch annotation so you can enrich your full pipeline at scale.
- Synthetic data: Scale AI is a leading provider of synthetic data. From even smaller datasets, they can identify patterns and augment your datasets to scale with your needs.
- Evaluation: Evaluate your model using professionally generated prompts to identify weak spots and vulnerabilities. This allows you to sharpen and curate your model output.
Scale AI provides specialized curation solutions for processing and model enrichment. They don’t provide you with raw data. Scale AI gives you the resources to enrich your datasets and sharpen model output.
Mostly AI
Mostly AI specializes in synthetic data. They allow teams to create artificial but realistic datasets that can be used without exposing sensitive information. Mostly AI is a solid provider for teams with privacy or regulatory concerns. Rather than tapping a raw data source, your team gains access to AI-generated synthetic data — ready for AI usage.
Processing and enrichment
- Synthetic data: Generate structured datasets that mimic real-world patterns across domains such as finance, healthcare and consumer analytics.
- Privacy focused: Rather than exposing sensitive data directly to a model, your AI system can use synthetic data for the same inferences with less concern about leakage.
- Balance and scale: Using their platform, you can grow smaller datasets and even rebalance skewed data. This offers a new lens on data curation compared to more traditional companies.
Mostly AI is best for companies with sensitive, small or unbalanced datasets. Their synthetic data generation platform allows teams to achieve curation from a different angle.
Apify
Apify sits mostly in the sourcing portion of our curation process. They offer access to over 6,000 Actors so you can make use of prebuilt scrapers. These Actors are made by both Apify and community developers so quality varies across the Actor store depending on the creator. They also offer out-of-the-box integrations with storage methods, coding platforms and workflow applications.
Sourcing
- Actors: User Actors to run scrapers on demand and scale your data collection as needed. Push button and get structured data.
- Integrations: Connect your curation pipeline to GitHub, Slack, Gmail, Airtable and more.
- Anti-blocking: Using their anti-blocking system, you can gain access to some of the most difficult data sources on the web.
Apify is a great service for data sourcing. When utilizing their Actor platform, integrations and anti-blocking, you’ll be able to tap almost any data source and output structured data. Combine this with a processing and enrichment service for full-stack curation.
Full breakdown of providers and tooling
| Provider | Focus area(s) | Strengths | Limitations | Best fit |
|---|---|---|---|---|
| Bright Data | Sourcing + Processing | Enterprise-grade pipelines, compliance, managed APIs, curated datasets | Premium pricing, geared toward larger teams | Enterprises that need compliant, production-ready web data |
| Appen | Sourcing + Processing | Human workforce for real-world data collection, annotation across modalities, evaluation services | Slower, less automated, “legacy” model | Projects that require human-collected or domain-specific data |
| Hugging Face | Processing | Open-source hub for models and datasets across many modalities | No raw data sourcing, quality varies by contributor | Teams prototyping or fine-tuning with community datasets/models |
| LAION | Processing | Large-scale open datasets, open-source models, tools like img2dataset | Limited scope, experimental reimplementations, no enterprise guarantees | Researchers and teams experimenting with large open data |
| Scale AI | Processing | Annotation at scale, synthetic data, model evaluation | No raw data, premium pricing, focused on enrichment | Enterprises building supervised models or evaluating LLMs |
| Mostly AI | Processing | Privacy-focused synthetic data, rebalancing, scaling small datasets | Doesn’t provide raw data or annotation | Companies with sensitive, small or unbalanced datasets |
| Apify | Sourcing | Marketplace of 6,000+ prebuilt scrapers (“Actors”), automation, integrations | Incomplete tool — requires pairing with annotation/enrichment | Developers or mid-sized teams needing fast web data extraction |
Conclusion
Data curation is not something to ignore. Every project has different needs. Larger projects need an end-to-end enterprise provider like Bright Data to tap any source and curate their data all throughout the pipeline.
Open source projects like Hugging Face and LAION allow teams to skip the sourcing process and springboard straight into model training and fine-tuning. Scale AI and Mostly AI offer tailored solutions for teams needing synthetic data or highly specialized annotation. You can even use workforce driven data curation with Appen.