For massive training data, it provides an open, easy-to-use and efficient AI DataHub (Git for AI Data) to serve the demands of AI developers' data management and enterprise asset management.
1. Tens of billions of data management: provide a platform to manage tens of billions of unstructured data to boost the training inference of AI large models.
2. Second-level retrieval and mining: provide retrieval of large-scale unstructured data to achieve second-level return and quickly mine new samples.
3. Guarantee data security: provide a complete set of solutions for data security in AI scenarios to guarantee the privacy compliance of AI data.
4. High-quality public datasets: provide high-quality public datasets in the industry and quickly load data via PythonSDK tools.
Build Git for AI Data to perform full lifecycle management of unstructured data in the enterprise, enabling version management, multi-user collaboration and data sharing.
Every aspect of data management is protected by security measures, such as permission control, digital watermarking, data desensitization and compliance authorization, to protect data security.
Datasets are ready to use, no need to download to local and can be loaded directly through a line of script. It enables high-speed AI training with the acceleration of AI caching service.
Retrieve unstructured data in natural language with the power of AI large models and return it in seconds to mine valuable business data.
Build Git for AI Data to perform full lifecycle management of unstructured data in the enterprise, enabling version management, multi-user collaboration and data sharing.
Every aspect of data management is protected by security measures, such as permission control, digital watermarking, data desensitization and compliance authorization, to protect data security.
Datasets are ready to use, no need to download to local and can be loaded directly through a line of script. It enables high-speed AI training with the acceleration of AI caching service.
Retrieve unstructured data in natural language with the power of AI large models and return it in seconds to mine valuable business data.
Build Git for AI Data to provide data iteration version management, branch collaboration and dataset sharing from data import and data processing to the full lifecycle of data use.
Single-line script can load dataset via SDK tool to boost high-speed model training. CLI tool can achieve version and branch management to control data iteration.
Provide image retrieval function in natural language based on large models and retrieve sample data flexibly based on metadata, annotated data, predicted data and customized tags.
On the Web end, multimodal data and annotated data can be visualized easily, an overview of the dataset can be viewed quickly and file operations on the Web end can be performed.
Build Git for AI Data to manage the data lifecycle
Provide CLI tools for version and branch management
Extract file features to avoid redundant storage
Tags on datasets for fast retrieval
SDK tools ready to use to load datasets
Provide large models with corresponding public datasets
Fine-tuning datasets to perform full lifecycle management
Multi-class tagging of samples for fast retrieval mining
Natural language retrieval based on large models
Permission control to manage data access
Digital watermarking to prevent data leakage
Data desensitization to guarantee data compliance
Build Git for AI Data to manage the data lifecycle
Provide CLI tools for version and branch management
Extract file features to avoid redundant storage
Tags on datasets for fast retrieval
SDK tools ready to use to load datasets
Provide large models with corresponding public datasets
Fine-tuning datasets to perform full lifecycle management
Multi-class tagging of samples for fast retrieval mining
Natural language retrieval based on large models
Permission control to manage data access
Digital watermarking to prevent data leakage
Data desensitization to guarantee data compliance
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