Why AI Needs Data: AI Training and Data Infrastructure
Henrik Olaf Nilsson
2026-08-24 16:00
Key Takeaways:
Have you ever wondered why some AI systems can accurately answer questions, recognize images, or even analyze business documents, while others frequently produce inaccurate answers?
In many cases, the problem is not entirely with the model itself. An AI model is like a beginner: it needs to process large amounts of relevant data to learn patterns. Insufficient data, limited samples, or too much inaccurate information can directly affect its performance.
Businesses also face practical challenges when deploying AI: finding usable training data, processing and annotating raw data, and storing and managing massive datasets. An AI project is therefore about much more than building a model. From data collection and model training to underlying storage, it requires a complete data infrastructure.
Humans understand the world through life experiences, learning, and accumulated knowledge. AI does not have real-world experiences in the same way. Its ability to learn patterns mainly depends on training data.
During training, AI processes large numbers of samples repeatedly and identifies relationships between inputs and outputs. Simply put, data is fed into a model, the model continuously adjusts during training, and eventually develops the ability to perform specific tasks.
Algorithms and models provide the framework for how AI processes information and performs calculations, while data provides the examples it learns from. Even with an advanced model architecture, insufficient or low-quality training data can make AI-generated answers, image recognition results, and predictions less reliable.
As AI applications continue to expand, the types of data they need are becoming increasingly diverse. Large models may require text, images, audio, and video, while businesses developing AI for specific applications may also need industry materials, business records, and other data closely related to their operations.
As AI capabilities continue to evolve, the industry is also demanding greater data volume, quality, and freshness. Data is no longer simply raw material for training. It has become a core factor determining how effectively AI can be applied.
The first principle when selecting training data is to match it with the actual task.
A chatbot needs sufficient conversational text. An image recognition system needs images and corresponding labels. Speech-to-text applications require audio recordings and their corresponding transcripts. Prediction, classification, and recommendation tasks often rely on structured data such as spreadsheets, user behavior, and business records.
Matching the use case alone is not enough. Data also needs to have sufficient volume and diversity. If there are too few samples, AI may not learn the underlying patterns effectively. If the data covers only a limited range of scenarios, the model may struggle when it encounters different situations in real-world applications.
If training samples are highly repetitive, the model has limited exposure to different scenarios. Its performance may decline significantly when it encounters situations it has never seen before. Ideally, the distribution of training data should closely reflect the real-world environment in which the AI will eventually operate.
However, this does not mean that more data always leads to better results. Simply expanding the dataset does not guarantee a linear improvement in model performance. If large amounts of inaccurate, irrelevant, or low-quality content are added, they may instead interfere with training.
High-quality training data is not about pursuing volume alone. It needs to match the business task and strike a balance between scale, quality, and diversity.
AI training data refers to individual samples used by a model for learning. The model extracts features and identifies patterns from these samples, gradually establishing relationships between inputs and outputs. Depending on the task, training data can include text, images, audio, video, or structured data such as tables and numerical records.
When large numbers of training samples are organized and collected according to consistent standards, they form a dataset. A complete dataset is usually divided according to its purpose and may include labels, categories, and other metadata.
Training data and datasets are often confused. Training data refers to individual samples that can be used for learning, while a dataset is the complete collection formed after these samples have been organized and standardized.
| Comparison | Training Data | Dataset |
| Core meaning | Individual samples used by AI for learning | A complete collection of organized data |
| Focus | The content of individual samples | Organization and structure of the entire dataset |
| Common contents | Text, images, audio, and other materials | Training, validation, and test sets |
| Main purpose | Provide learning materials | Organize and manage the complete data collection |
In practical AI projects, a dataset is generally divided into three parts: the training set, validation set, and test set.
AI systems process many different types of data, and each type can support different applications.
Text data: Articles, conversations, documents, instructions, and web content are among the main data sources for large language models. They support tasks such as conversations, content generation, and question answering.
Image data: Photos, screenshots, product images, and medical images can be used for object recognition, image classification, and AI image generation.
Audio and speech data: Voice recordings, conversations, and environmental sounds, often paired with transcripts, are used for speech recognition and AI voice generation.
Video data: Video combines visual, audio, and subtitle information and is commonly used for video understanding and action recognition.
Structured data: Tables, numerical data, user behavior, and sales records are widely used in traditional machine learning for prediction, classification, and recommendations. This type of data is often stored in relational databases or data warehouses.
Multimodal data: Modern multimodal AI can work with several types of data at the same time, such as images paired with text descriptions, videos with subtitles, or audio combined with text. These combinations are becoming increasingly common in AI applications.
Most raw data collected for AI projects does not come with labels or structured information designed for a specific business task. It therefore needs to be cleaned, organized, and annotated before it can be used for AI training.
Data annotation is the process of adding labels to raw data to describe its content or business attributes, helping the model understand what the data represents in a particular application.
Different tasks require different annotation methods. Image tasks may require objects in an image to be identified and marked. Text tasks may involve labeling topics, intents, or emotions. Speech tasks may require audio recordings to be transcribed into text.
There are three common annotation approaches: manual annotation, automated pre-annotation, and human-machine collaboration. Manual annotation provides greater control over accuracy but can be inefficient when processing large datasets. Automated pre-annotation uses models to generate initial labels, which are then reviewed and corrected by humans. Human-machine collaboration combines the two approaches to balance quality and efficiency.
For complex tasks with high annotation costs, active learning can also be used. The model first identifies samples that it finds difficult to classify and sends them to human annotators. This allows human effort to focus on high-value samples and reduces repetitive work.
Data annotation is more than simply adding labels. It is a critical step in preparing training data, and annotation accuracy directly affects dataset quality and the final training results.
There is a well-known principle in the AI industry: garbage in, garbage out. When the quality of input data is poor, model outputs can also be affected. AI learns patterns from training data, so problems within the data can eventually appear in model performance.
If a dataset contains a large amount of inaccurate information, the model may learn incorrect patterns. Incorrect labels can lead to recognition and prediction errors, while excessive duplicate, irrelevant, or invalid content can reduce the overall value of the dataset.
Data cleaning is therefore an important part of the preparation process. Errors, duplicate samples, and irrelevant data should be identified and removed before training.
If an AI model encounters too few scenarios during training, it may struggle when deployed in real-world environments.
For example, if training samples are concentrated in a single type of scenario, model performance may decline when it encounters different regions, expressions, or usage environments. Training datasets should therefore cover as many relevant real-world business scenarios as possible instead of relying on a small number of simple samples.
Duplicate samples and data leakage are common problems during AI training. If data that should only be used for testing accidentally enters the training set, data leakage occurs.
The model may then achieve an unusually high evaluation score because it has already seen some of the test data. However, its performance may drop significantly when it encounters genuinely new data.
Training, validation, and test sets should therefore be kept separate, with the same or highly similar samples prevented from appearing across different sets.
In addition to avoiding errors, duplicates, and data leakage, high-quality training data should provide strong accuracy, relevance, diversity, completeness, consistency, and freshness.
These factors collectively determine whether data is truly suitable for model training. Even a massive dataset may have limited value if it lacks accuracy or relevance to the actual business task.
After data is collected, cleaned, and annotated, businesses still need to solve the practical challenge of storing and managing it so that training tasks can access the data efficiently.
AI training often processes massive amounts of raw materials and processed datasets. Images, audio, and video can require substantial storage capacity, while data volumes continue to grow as AI models and business applications evolve.
Without suitable infrastructure, storage, data transfer, and management can all become bottlenecks that slow down training.
AI projects need high-capacity storage systems to centrally store raw materials, processed datasets, and multiple versions of training data so that training tasks can access them when needed.
However, different types of data have different access frequencies and levels of importance. Frequently accessed data that is actively used for training requires high-performance storage, while historical data that is no longer actively used can be placed in lower-cost archival storage.
Using tiered storage based on access frequency can help balance performance and cost.
AI training does not only require data to be stored; it also needs data to be delivered quickly. During training, GPUs continuously process large amounts of data. If storage cannot provide data quickly enough to keep up with GPU computing capacity, GPUs may sit idle while waiting for data, wasting computing resources and extending the overall training cycle.
Infrastructure therefore needs to account for data throughput between storage and computing resources. In large-scale training scenarios, the speed and stability with which data moves from storage to computing clusters can directly affect training efficiency.
In simple terms, storage capacity determines whether data can be stored, while read and transfer performance determines whether the data can be delivered to the model in time.
Storing data does not mean that it is immediately ready for training. Raw data usually needs to be cleaned, deduplicated, and converted into appropriate formats before being organized into datasets for specific tasks.
As projects evolve, multiple versions of the same dataset may also be created. Version management is therefore needed to ensure that training tasks use the correct data.
For businesses, AI data infrastructure is not simply about storage. It also needs to support data processing workflows and dataset version management, providing end-to-end support from raw materials to usable training data.
Business AI data can include internal documents, user information, commercial assets, and copyrighted content. Without appropriate access controls and security measures, risks may arise throughout the storage, transfer, and usage process.
Infrastructure therefore needs to provide access control and data protection while addressing privacy, copyright, and authorization requirements. When external or publicly available internet data is used for AI training, businesses should also clarify its sources and permitted uses to ensure that the data can be used appropriately.
Building an AI project starts with determining where the data will come from. Businesses can first make use of their own accumulated data, including internal documents, customer service conversations, product information, and business records.
They can also use public datasets and academic databases, or obtain authorized third-party data through commercial purchases and partnerships.
Public web pages, articles, and product information are also important sources of AI data. Internet data is extensive, frequently updated, and covers a wide range of industries and regions, making it useful for market research, industry analysis, and AI training.
However, websites in different regions may vary in page content, language versions, and information updates. If a project needs to continuously collect public web data from multiple markets, it may need IP resources suited to the target markets to better observe publicly available information from different regions.
In such scenarios, residential IPs can serve as a basic resource for collecting public web data across different markets. For example, 1024Proxy provides residential IP resources across multiple regions, allowing teams collecting public web content from different markets to select IPs based on their specific needs.
Raw web content should not be directly fed into AI training. It needs to go through filtering, cleaning, and quality checks first. For projects that continuously use data from multiple sources and regions, maintaining reliable sources, valid content, and a stable data management process is equally important to building a complete AI data system.
In the future, competition in the AI industry will not be determined by model capabilities alone. Businesses’ ability to acquire, manage, and use data will become increasingly important.
At the same time, the industry is facing new data challenges. Access to high-quality public data is becoming more difficult, while synthetic data is emerging as an important supplementary source but still carries risks related to noise and repetitive patterns. Meanwhile, regulations surrounding global data flows continue to evolve, requiring businesses to pay greater attention to data sources, permitted uses, and compliance when collecting and using data across different regions.
Businesses that can consistently obtain reliable data and make effective use of it will be better positioned to bring AI into real-world applications.
Models determine how AI learns, while data determines what it can learn.
Training data refers to individual samples used for AI learning, which can include text, images, audio, and other types of data. A dataset is a complete collection of samples organized according to consistent standards and is typically divided into training, validation, and test sets.
Not necessarily. Data volume is only one factor affecting training results. Large amounts of inaccurate, duplicate, or irrelevant data can negatively affect model performance. Accuracy, diversity, and business relevance are equally important.
Data annotation means adding labels that describe the content or attributes of raw data, helping the model understand what the data represents. For example, marking objects in an image or adding intent labels to text are common annotation tasks.
In AI training, data leakage usually occurs when data intended for validation or testing accidentally enters the training process. The model may then see test samples in advance, resulting in inflated evaluation scores and weaker performance on genuinely unseen data.
Usually not. Web data should be filtered, deduplicated, cleaned, and quality-checked before training. Businesses should also consider data sources, copyright, privacy, and authorization requirements to ensure that the content can be used appropriately.
