Using Web Data to Train AI Models

In the two-plus years since AI solutions exploded onto the business stage, we’ve moved through the first few stages of the hype cycle, and its business value is clear. Now we’re seeing companies scramble to build their own custom models, so as to preserve data security, improve operational efficiency, and increase the accuracy of their outcomes.

And as a direct result, there’s a lot of unseemly shoving going on around training data. AI models are extremely sensitive to the rule of “garbage in, garbage out,” so everyone needs high-quality, trustworthy training data to drive positive model performance.

Many AI developer teams head straight for open-source datasets, which are easy to access and whose reliability is well proven. But this isn’t enough to serve the training data needs of scores of enterprises, especially when you’re building on top of foundational models that have already been trained on this data.

What’s more, if every company draws on the same datasets, the outcomes are not sufficiently differentiated, and if you want to refine your models for specific use cases, you need individualized training datasets that are relevant to those use cases.

In a recent interview with AiThority, Bright Data CEO Or Lenchner explains that web data should be the first port of call for companies that are looking to build effective, accurate, and specific AI models. Datasets that are sourced from the web are diverse, real-time, ethical and less likely to suffer from biases, and there’s plenty out there for everybody’s needs.

“In the long run, responsible and seamless data pipelines won’t just protect businesses,” Lenchner asserts, “they’ll define the industry leaders.” With the right tools and guardrails, you can gather the training data you need without hassle and without overstepping compliance requirements.

Web Data Is Quality Data

AI training data needs to be trustworthy, but it also needs to be fresh and diverse. “AI models that are trained on limited, outdated, or biased datasets will eventually produce outputs that are likewise limited, outdated, and biased,” warns Lenchner. “Data keeps on changing. Consumer behaviors shift, markets evolve, and new trends emerge on a daily basis. So businesses that rely on static datasets will always be a few paces behind the real world.”

This is a big advantage of sourcing data from the web, Lenchner believes. “To keep your AI models working effectively, you need scalable, diverse data constantly flowing from multiple sources, industries, and geographies,” he says. Millions of people are adding and refreshing web data every minute of the day, making it inherently diverse, which is crucial to prevent bias. When you collect data in real time, it’s always an up-to-date reflection of current realities.

Lenchner adds that sourcing real-time data from the web also gives enterprises vital control over the data they use. When you’re finetuning company-specific AI models, he emphasizes, it’s extremely important to input precise datasets. As he puts it, “a fraud detection system doesn’t need the same data as a recommendation engine, and a healthcare AI requires entirely different inputs than an e-commerce chatbot.”

Thanks to the enormous diversity of the internet, web data can deliver on these needs. As long as you set your parameters correctly, you can draw the datasets that meet your particular use cases. “Businesses that can fine-tune their data pipelines to choose the sources, formats, and parameters that matter most will build smarter, more efficient AI that delivers real business impact,” he says. “Those that don’t will struggle with inefficiencies, inaccuracies, and wasted resources.”

Web Data Is Manageable

As every data science team knows, collecting the data is only the beginning. You still need to validate it, clean and preprocess it, and structure it correctly, before it can be put to use for AI training. Manual processes and/or pipelines that send data into silos “results in operational inefficiencies, delayed AI training, and lagging innovation,” cautions Lenchner.

“The only way to keep AI models relevant is by using automated, scalable data collection that continuously adapts to real-world changes,” he says. He strongly recommends building automated data pipelines which integrate smoothly with your AI frameworks, cloud environments, and MLOps platforms. This makes it possible to scale up operations so you can handle the volume of data delivered from web data sourcing.

“In AI, speed and precision are everything. When data collection is directly connected to preprocessing, storage, and AI training workflows, businesses can move faster, reduce costs, and improve model accuracy,” says Lenchner. “Companies that get this right will build AI systems that don’t just react to the world — they help shape it.”

Web Data Can Be Compliant and Ethical

One hurdle that sometimes puts people off from using web data as a source for training data is concern around compliance with data privacy regulations and copyright laws. Lenchner acknowledges that some data providers fail to treat compliance with the respect it deserves.

However, he emphasizes that integrating ethical data collection practices is possible, undemanding, and even beneficial.

“It’s a sad truth that some companies treat compliance as a legal box that they need to check, instead of seeing the competitive advantage that it offers,” says Lenchner. He points out that smart businesses “bake compliance into their data strategy from day one, ensuring they can scale AI operations without disruption.”

In his view, companies that adhere to transparency and only work with data providers that maintain ethical practices are unlikely to be faced with lawsuits or regulatory penalties. In fact, he says, “in the long run, responsible data practices won’t just protect businesses, they’ll define the industry leaders.”

Web Data Is the Gold Standard for Sourcing Training Data

As a never-ending source for fresh, diverse, and unbiased datasets, web data is an ideal resource for training data. It’s well worth investing in building the data pipelines and responsible practices that can handle real-time data sourced from the web. As AI model adoption increases and training data becomes ever-more valuable, your data intake pipeline will be your competitive edge.