In 2024, China revised its approach to data and AI. The government established a national body to unify industry data standards and facilitate data sharing across industries. This has led to a huge increase in AI efficiency without sacrifice of quality, despite constraints. Chinese developers have shown their ingenuity as they optimized data collection and revamped processes and policies in data handling to achieve the amazing AI outcome that is Deepseek.
The emergence of Deepseek and other Chinese LLMs shows that data plays a crucial role in driving AI. These models prove that AI development is less about the complexity of algorithms and access to high-end hardware and more about the quality and timeliness of training data.
Enterprises looking to build their own AI models cannot focus solely on computing power and must allocate resources to building a robust strategy for collecting the model training data. While there are plenty of big training data libraries out there, Deepseek shows us it’s more about quality than quantity. For an LLM to be useful, it needs to be trained on relevant and dynamic data.
Gathering, preparing and feeding data to models for training is not a piece of cake though. Organizations need to overcome strategic challenges such as technical complexities, data governance and security, and ethical implications. Here’s a quick guide to building more useful AI models by addressing the challenges of getting and working with high quality and timely data.
Overcoming Scalability and Resource Constraints
Scalability and resource constraints are inevitable in AI training especially when building custom models and complex systems. The inability to collect and process data in large volume and the resulting computational demands can create bottlenecks that makes it hard to build usable AI models.
To overcome scalability and resource constraints, AI training can leverage cloud computing for infrastructure and on-demand resources. Distributed training frameworks offer a good alternative, allowing AI models to be trained across multiple machines for better efficiency.
AI training teams can also use open-source and extensible data scraping solutions like Scrapy, which allows scalable data extraction tailored to specific needs.
Addressing Data Inaccuracy, Staleness, and Bias
High quality data is accurate, complete, recent and bias free which is hard. First data collection may be plagued with sampling errors and data collection biases. The training data collected may only represent one or a few aspects of a topic. There may be bias towards for example formal language (if you source data from academic journals) or realistic imagery (if you source data from nature publications).
Another problem is we can’t get the full data, so we estimate and predict inaccurately. There can be errors in data input and measurement like in the case of faulty and poorly calibrated sensors. Data processing issues like errors in data cleaning and transformation can also happen. And there are problems with definitions that change over time or events that creates new interpretation of data.
Organizations need to managed their data collection, validation and processing procedure. An optimized data collection tech stack can help systematic data handling to make sure the data collected is relevant, complete and timely. Bright Data, a web scraping solution, provides pre-collected datasets across various topics and web scraping APIs and networks of residential proxies so developers can collect data from websites and social media worldwide.
Manual sourcing of data from the internet is impractical and inefficient. We need to use tools with built-in verification and approval mechanism to maintain data accuracy and relevance. Data scraping need to cover the full scope of information publicly available and be updated in real time to ensure timeliness.
Stale and bad data affects the quality of machine learning model.
Capturing and Preparing Diverse Forms of Data
AI training data is not limited to text and audio. High quality data is wide range of information including still and moving images, illustrations and models, sensor readings, time-series data and analysis. While there are tools that can process various forms of data, they may not capture all the required information reliably and accurately. Specialized solutions can ensure precision, completeness and scalability.
When dealing with images for example, we need to do data preprocessing to convert raw data into a format that is usable for AI training. PyTorch for instance provides the functions to clean, format, transform and augment visual data to include rich computer vision descriptions making it meaningful for AI training.
Similarly, the NLP solution SpaCy is useful for large scale information extraction tasks, processing large volume of web dump to prepare sentiment analysis output for further AI training.
Data cleaning, augmentation and synthetic data generation can’t fix the inaccuracy caused by bad data scraping solution. So we need to evaluate if the data collection workflow in place is enough for the specific requirement of the project.
Data Privacy and Compliance
AI should never come at the expense of data privacy and compliance to data and intellectual property regulations and laws. A big class action suit is in the works with a UK firm gearing up for claims against Google and Microsoft for using private data in their AI training datasets.
Bright Data and Scrapy have features and functions to address data privacy. For example, data collection workflows can be set to follow the instructions in a website’s robots.txt file. As a matter of policy Bright Data can only be used to collect publicly available data on the open web, making compliant scraping easier.
Read the fine print of scraper APIs to ensure you comply with data laws and avoid legal entanglements. Real-time data needs to be obtained within legal and ethical boundaries.
Conclusion
You can definitely build usable AI models with real-time data. Organizations need to have a systematic and scalable way to collect and process data to avoid the pitfall of inaccurate, incomplete, stale, biased and compliance data.
