Unstructured data significantly impacts the big model storage architecture due to its high volume, diverse formats, and lack of predefined schema. Unlike structured data (e.g., databases), unstructured data includes text, images, audio, video, and logs, which require specialized storage and processing approaches to be effectively utilized by large models.
Storage Volume & Scalability
Unstructured data often constitutes the majority of data in AI/ML workflows (e.g., training datasets for vision or NLP models). Big models need scalable storage to handle petabytes of unstructured data, such as image repositories for computer vision or text corpora for language models.
Example: A large language model trained on web-crawled text (unstructured) requires distributed storage to manage terabytes of raw documents.
Data Preprocessing & Feature Extraction
Before feeding unstructured data into a big model, it must be preprocessed (e.g., tokenization for text, resizing for images). The storage architecture must support efficient data pipelines for transformation.
Example: Storing raw images in an object store while using a separate compute layer to extract features (e.g., embeddings) before model training.
Metadata Management
Unstructured data lacks structure, so metadata (e.g., labels, timestamps, tags) is crucial for retrieval and training. The storage system must efficiently index and query metadata.
Example: A recommendation system storing user-generated videos (unstructured) with metadata (e.g., user preferences) to improve model accuracy.
Access Patterns & Latency
Big models may require random or batch access to unstructured data. The storage architecture must optimize for different access patterns (e.g., high-throughput for training, low-latency for inference).
Example: A generative AI model retrieving relevant images from a large dataset (unstructured) with low-latency access during inference.
For handling unstructured data in big model workflows, Tencent Cloud COS (Cloud Object Storage) is ideal for scalable, cost-effective storage of large files (images, videos, logs). For metadata management, Tencent Cloud TDSQL or Tencent Cloud Elasticsearch Service can help index and query unstructured data efficiently. Additionally, Tencent Cloud TI Platform provides integrated tools for preprocessing and training models on unstructured data.
By designing storage architectures that account for unstructured data’s unique challenges, big models can achieve better performance, scalability, and cost efficiency.