As an industry expert with over a decade of experience in web data extraction and proxy services, I‘m often asked – what are the best ways to collect data to train AI and machine learning models?
The quality of the training data directly impacts the performance of machine learning algorithms. Without robust, relevant, and unbiased data, even the most advanced ML models will fail to deliver value.
In this comprehensive guide, I‘ll share my proven insights on the top 6 data collection methods for AI/ML, to help you make informed decisions:
The Growing Need for AI Training Data
The appetite for quality data to fuel AI/ML initiatives has skyrocketed globally:
- As per MarketsandMarkets, the AI training data market will reach $1.8 billion by 2024, growing at a CAGR of 23%
- Gartner predicts that by 2022, 70% of organizations will use AI techniques in their research teams, up from less than 40% in 2021.
- Accenture estimates that AI could double annual economic growth rates worldwide by 2035, increasing labor productivity by 40%.
The growing integration of AI is driving massive demand for training data (Source: Statista)
This burgeoning integration of AI into business processes, products, and services makes a reliable data supply chain crucial. Next, let‘s discuss popular techniques for collecting AI training data at scale:
1. Crowdsourcing for Labeling & Annotation
Crowdsourcing has emerged as a preferred way to annotate and label large volumes of data cost-efficiently by distributing tasks to a global crowd of contributors.
Some advantages of crowdsourcing data tasks:
- Speed: AI training datasets can be labeled 25-40 times faster than in-house
- Cost: Crowdsourcing costs at least 80% less than hiring full-time data annotators
- Scale: Direct access to thousands of qualified contributors on demand
- Domain expertise: Experienced firms have specialists (e.g. doctors for medical imaging)
For example, Figure Eight claims to have over 1 million highly vetted contributors who can label complex data like self-driving car footage at massive scales.
However, key challenges with crowdsourcing data tasks include:
- Ensuring data security when relying on external annotators
- Implementing QA mechanisms to validate labeling quality
- Mitigating biases through careful reviewer selection and monitoring
Crowdsourcing enables distributed annotation of training data (Source: Lionbridge)
Expert Tip
When using crowdsourcing platforms, start with small annotation samples for quality validation before proceeding to large batches. Also, ensure robust anonymization for sensitive datasets.
2. Private, In-House Data Collection
For highly specialized business problems involving confidential data, companies often choose to manage data collection internally. This requires significant investments in data science and engineering talent.
In-house data teams are indispensable for:
- Scraping and extracting data from proprietary internal systems
- Customizing data pipelines tailored to unique AI needs
- Annotating complex industry-specific datasets
- Ensuring data privacy and compliance
According to a McKinsey survey, top AI adopters report 50% larger data science teams than mainstream adopters. But scaling internal data ops can be challenging:
- Data engineering bottlenecks may slow the pace of collection
- Re-training is needed to adapt to evolving annotation schemas
- Limited data diversity from focusing on proprietary sources
Expert Tip
Balance in-house data collection for control with crowdsourcing for scale and diversity. Also, invest in data tooling and automation to offset resource constraints.
3. Leveraging Pre-existing Datasets
An expedient option is using commercially available datasets from sources like Kaggle and Figure Eight to jumpstart AI initiatives:
| Data Source | Sample Datasets |
|---|---|
| Kaggle | Labeled images, text corpora, tabular data |
| Figure Eight | Text, images, audio, video |
| AWS Data Exchange | Retail, financial, healthcare, media data |
| Azure Open Datasets | Public open government data |
Pre-packaged datasets help quickly prototype models and speed up development cycles. According to an Alation survey of data professionals, 69% leverage external third-party datasets to complement internal ones.
However, common downsides of third-party data include:
- The risk of stale, redundant data that lacks relevance
- No insight into sourcing, collection and QA methodologies
- Potential hidden biases or sampling issues
Expert Tip
Thoroughly evaluate external datasets and augment with recent domain-specific data to overcome deficiencies. Use prepackaged data for faster model prototyping before migrating to production-grade custom data.
4. Web Scraping & Crawling at Scale
For many real-world AI use cases, public web data can offer invaluable training insights when harvested at scale using scraping and crawling techniques. As per a ScrapeHero report, the internet contains over 4.1 billion pages and 2 billion images ripe for extraction.
Some examples of public web data used to train AI models:
- Scrape online menus to create a food recognition system
- Crawl auto listings to train price estimation models
- Extract product descriptions to build a recommendation engine
Web scraping can be done either manually or automatically using tools like Python Scrapy, Octoparse, Beautiful Soup, etc. However, I recommend an expert-managed scraping service since:
- Commercial tools require programming knowledge
- Manual scrapping is slow and labor-intensive
- Scraped data needs cleaning and structuring
For instance, our AiMultiple scraping service delivered over 15 million scraped records for an e-commerce price tracking client, through an optimized combination of headless browsers and proxies that bypass scraping defenses.
Common pain points to address with any web scraping initiative:
- Avoiding detection with careful crawling and proxies
- Handling dynamically loaded content
- Ensuring relevance over time as sites evolve
Expert Tip
When scraping public websites, employ ethical practices and respect robots.txt policies. Use scraping judiciously to supplement first-party data, not as the sole input for production AI systems.
5. Leveraging Platform APIs and Open Data
Many online platforms provide APIs for structured data access at scale to power third-party applications:
- Social media APIs like Twitter, Reddit
- Review platforms like Yelp, TripAdvisor
- E-commerce sites like Amazon, eBay
Government open data portals like data.gov and data.europa.eu also provide a wealth of training data for civic AI applications.
For instance, the Google Maps Platform provides APIs for place data, geographic context, and traffic statistics that can help train delivery optimization models.
Benefits of leveraging platform APIs and open data:
- Avoid extraction/scraping efforts to directly access structured data
- Leverage meticulously curated open datasets
- Access specialized domain data hard to obtain otherwise
However, a key downside is the limited control and visibility into API sampling, coverage, and consistency over time.
Expert Tip
Evaluate API data thoroughly to ascertain suitability and complement with additional data sources as needed for sufficient coverage. Don‘t rely solely on third-party APIs for production AI systems.
6. Data Generation Using GANs
An emerging technique that shows immense promise for AI data collection is Generative Adversarial Networks (GANs). GANs can automatically generate synthetic data for scenarios lacking real-world samples.
The core process involves:
- A generator neural net creates simulated data resembling the actual dataset
- A discriminator net tries to distinguish the fake data
- Back-and-forth training enhances the generator‘s ability to create realistic data
For instance, GANs have been used by pharma researchers to generate molecular data for specialized drug discovery models lacking comprehensive training datasets.
GAN architectures like StyleGAN and CycleGAN have shown remarkable capabilities:
StyleGAN can produce highly realistic simulated facial images (Source)
Benefits of using GANs:
- Expand datasets by generating plausible new samples
- Address gaps for rare scenarios lacking data
- Enable data anonymization for confidential tasks
However, evaluating sample quality and controlling biases remain key challenges.
Expert Tip
Use GANs to complement real data, not replace it fully. Continuously monitor the generated samples for aberrations and overfitting risks.
Key Considerations for Your AI Data Sourcing
Based on my decade of experience enabling data collection for Fortune 500 AI teams, I recommend:
- Hybrid strategies: Blend internal, external, web, and generated data for optimal diversity and coverage.
- Focus on data relevance: Carefully evaluate datasets before purchasing or scraping to ensure applicability.
- Embed quality assurance: Implement mechanisms like sampling, statistical validation, manual reviews to ensure data integrity.
- Monitor for concept drift: Ground truth changes over time. Continuously assess if datasets represent latest domain realities.
- Lifelong learning: Plan to expand datasets iteratively as models uncover new learning needs.
The end goal is democratizing business data for fueling AI reliably, not amassing datasets for their own sake. A thoughtful data supply chain is key to delivering measurable AI value while avoiding pitfalls.