Company Introduction

DataCanvas, with the mission of creating an intelligent exploration platform, aspires to empower global enterprises' intelligence upgrading. As a leader in China's AI foundation software field, the company focuses on developing autonomous and proprietary AI foundation software products and solutions, providing users with comprehensive AI foundation services.

DataCanvas aims to assist users in effortlessly achieving bidirectional enablement of models and data during digital transformation, enhancing enterprise decision-making capabilities in a cost-effective and efficient manner, thereby enabling enterprise-level, scaled applications.

AIFS (AIFoundationSoftware)

Addressing Challenges in the Ageof Large Models

The era of large models demands efficient computing power infrastructure and management of other infrastructure and resources. To address these challenges, DataCanvas has established a complete software system called AIFS (AIFoundationSoftware), consisting of four layers: model tools, large model capabilities, an AI foundation platform, and computing power management.

AIFS Layers

1. Computing Power Layer:
   • Leveraging the rapid advancement of domestic GPUs and significant progress in large models and GPUs by vendors like Huawei.
   • Constructing GPUCloud for unified management of heterogeneous GPU resources (NVIDIA and domestic GPUs).
   • Reducing engineering costs and enhancing resource utilization.
2. Model Tool and Database Layer:
   • Key technologies: DingoDB multi-modal vector database and AI development tools (APSFastLabel, APSLMB, APSLab, APSInference).
   • DingoDB: A distributed vector database, storing multi-modal data of any size, featuring high concurrency, low latency real-time analysis capabilities, and processing multi-modal data.
   • AI development tools enable efficient handling of challenges in the era of large models and rapid implementation of AI applications.
3. Large Model Layer:
   • DataCanvas Alaya, a large model supporting multiple data formats (video, image, text).
   • Capabilities to build small models, providing libraries of algorithms, scenarios, features, and metrics.
   • LMOPS: A comprehensive set of model building tools (PromptManager, LargeModelTraining, LargeModelServing).

AIFS Advantages

• Leading AI application building infrastructure platform.
• Supports building both large and small models.
• Covers the full lifecycle of large models (training, fine-tuning, compression, deployment, inference, and monitoring) and the entire process of small models.
• Supports various modeling modes, catering to the diverse needs of data scientists, developers, and business professionals.
• Facilitates collaboration among different roles on the AIFS platform, enabling seamless data handling and joint development, training, and deployment of models of any scale.

Model Building Toolkit

Empowering Large and Small Model Building

AIFS provides a comprehensive toolkit to empower the building of both large and small models.

Data Preparation

• Data sources: Universal data, industry data, private data, and instruction data.
• Data processing: Data cleaning, transformation, and augmentation, tailored to different data types.
• Data annotation: Manual and intelligent annotation.

Model Development

• Model selection: LLAMA, LLAMA2, Falcon, Bloom, etc.
• Training: Using prepared data or pretrained weights.
• Fine-tuning: Alignment operations.
• Efficient fine-tuning using PEFT for organizations with limited CPU resources.

Model Deployment

• Deployment options: Cloud, on-premises, or hybrid.
• Optimization techniques for inference latency and cost.

Model Management

• Centralized model repository.
• Version control and tracking.
• Monitoring and anomaly detection.