The race to build the best domain-specific large language models (LLMs) is on, and it hinges on one critical factor: the quality of the data they’re trained on.
Clean, diverse, and fresh data is not easy to integrate at scale without a well-structured data intake and engineering pipeline. Building an end-to-end pipeline that can reliably collect, clean, and deliver high-quality data will ensure the longevity and performance of your LLM.
Let’s break down how you can design a training-ready data pipeline with the help of dedicated tools for every step of the process, starting from data collection all the way to model deployment.
Data Collection at Scale
Collecting the data you need is the first hurdle when training an LLM. Sourcing information from the public web at scale is hard, as many high-value sources actively block automated scraping efforts. Additionally, you must navigate a web of legal and ethical considerations that can impact your approach.
Bright Data, a market leader in large-scale data collection, has found a solution to these issues. With a series of specialized APIs and tools, Bright Data solves the intake bottleneck by making it possible to extract a large quantity of highly relevant data from all sources, including complex websites that have dynamic, personalized elements or advanced anti-scraping measures in place.
With hosted browsers, millions of residential proxies, automatic retries and a smart solution for circumventing Captcha, Bright Data makes sourcing fresh, well-rounded information scalable. And although Bright Data allows you to import content that many media platforms prefer to keep restricted, the courts have upheld the principle that publicly available information is fair game, so you don’t need to worry about legal or compliance issues arising in the future. This is by far one of the most reliable ways to collect the best possible data for your LLM needs.
Data Cleaning and Preprocessing
The data you collect will rarely be ready for use straight away. However, it’s very likely to have duplicates, irrelevant entries, or other inconsistencies. These issues are very normal with raw data gathered through scraping, especially with large datasets that train LLMs. So, the next major challenge is to get that data cleaned up and ready for processing.
For smaller to mid-sized datasets, you can use OpenRefine to quickly clean and transform messy libraries. The tool has a point-and-click interface, making it easy to spot and fix issues without writing any code. All you have to do is load the data into the tool. Since it’s open-source, however, it may struggle with large datasets.
For larger LLM projects, you may need to move beyond GUI tools and build a dedicated Python data cleaning script. There are plenty of templates and libraries online that can help you automate large-scale cleaning tasks. The most popular include Pandas and PyJanitor.
From Intake to Training
Once the data is collected and cleaned up, it’s time to inject it into your training stack. For commercial-grade LLM projects, it’s best to store the clean data in cloud storage, such as AWS S3 or Google Cloud. From there, you can stream it directly into your training pipeline.
With libraries like Hugging Face’s datasets, or PyTorch’s torchdata, you can load data directly from your cloud repositories. Since you won’t have to migrate anything manually, you can stream the large datasets directly into your GPUs and TPUs, accelerating the training timeline.
Using the cloud is great for scaling the model as time goes on. Even if the data goes from 100 GB to 1 TB, you will just increase the storage capacity without having to reconfigure your pipeline. The data is centrally stored, so multiple machines can be assigned to pull different parts of the dataset in parallel. Imagine having to duplicate that amount of data across many machines. That would certainly not be time-efficient, nor sustainable if you plan to grow the model over time.
Orchestration and Pipeline Management
Collecting data, cleaning it, and injecting it into your training pipeline is certainly a chaotic and error-prone endeavor. For that reason, it’s best to automate and orchestrate the entire process with a dedicated workflow management tool.
A tool like Prefect lets you automate and manage every step of the data pipeline. Each stage of the pipeline, such as collecting the data with Bright Data or running a Python cleaning script, is defined as a task in Prefect. The tasks are linked together into flows, representing your full intake process from start to finish. With these automated flows established, you can schedule regular data updates, trigger cleaning scripts, and re-run tasks that fail, all without any oversight.
Of course, you will want to regularly monitor the pipeline to make sure everything is running smoothly or make adjustments as your data needs evolve. But from a practicality standpoint, incorporating a workflow automation tool here is a no-brainer.
Conclusion
The success of your LLM depends entirely on the quality of the data it’s trained on. Sure, creating an effective and trustworthy model has other challenges involved, but you will never get far if the data intake pipeline is lacking.
The tools and strategies we covered in this article will be key in building an effective pipeline that delivers relevant, clean, and scalable data consistently, setting your LLM training projects up for long-term success.
