AI Middle Platform

AI MIDDLE PLATFORM SOLUTION

Opportunities & Challenges Pain Points Introduction Advantages Values

Opportunities & Challenges

The early chimney style intelligent application construction method of enterprises caused fragmentation between systems, resulting in the inability to share AI capabilities. With the surge in demand for business intelligence, the problems of difficulty in reusing AI capabilities and high construction costs have become increasingly prominent. In addition, the high threshold for the use of AI technology makes it difficult for innovative technologies to quickly support business digital upgrades, leading to bottlenecks in the promotion and application of AI.

The AI platform can help enterprises integrate and optimize AI resources, improve development efficiency, and promote collaborative cooperation, thereby promoting business innovation and enhancing competitiveness. It is an indispensable and important component of enterprises in the field of artificial intelligence technology.

Pain Points

Quality and availability of data

Enterprises face challenges such as low data quality, lack of annotation, or inconvenient data storage and access, which can easily lead to poor performance of trained models or inability to meet actual business needs.
Complexity of technology and architecture

AI applications involve complex algorithms and technologies, requiring the integration of different algorithm frameworks and big data processing frameworks. Therefore, they face challenges such as difficult technology selection, complex system integration, and unreasonable architecture design.
Demand of computing & storage

The training and deployment of AI models require a large amount of computing and storage resources. Enterprises face problems such as high hardware equipment investment costs, limited computing resources, and difficulties in data transmission and storage, which puts certain pressure on their budgets and infrastructure.
Continuous iteration and updates

Enterprises may face complexity in model updates and deployments, as well as issues with version control and management. In addition, some of the AI applications require real-time updates and feedback, which poses challenges to the real-time performance and stability of the system.

Introduction

The DataCanvas AI mid platform solution breaks the chimney like ability fragmentation situation, enhances the integration of cross platform AI capabilities while improving AI engineering.This solution can break through the capability chimney, build a unified, secure, efficient, and interconnected AI capability system, comprehensively support the construction of AI asset sharing and co construction, agile business empowerment, AI governance and operation, and other aspects.

The unified AI engineering platform is based on a universal modeling platform, supplemented by rich professional modeling platforms, to build a complete MLOps system in the AI platform, fully supporting business empowerment of AI modeling.The unified service management platform uniformly manages the underlying AI capability services, providing functions such as service registration, service orchestration, and service monitoring, achieving unified management and orchestration of self-built, community, third-party, and other multi-source models.

Advantages

High performance computing management

The unified cloud clustering scheme and super fusion computing engine are used to realize the unified management of computing resources and the unified computing of multi-source heterogeneous data, multiple models, multiple languages, and multiple algorithms, and the model training efficiency is rapidly improved through large-scale distributed training.
Enabling agile business

Business oriented automatic modeling provides business specialists with the ability to freely combine modeling, and applies composable business architecture and composable technical support. It has realized short-term operation and more agile intelligent exploration and application.
Open cloud native architecture

With high flexibility and scalability, elastic computing and storage capacity，the platform has an open architecture and interface, supports connection and interaction with internal and external data sources, services and applications, and realizes data sharing and collaboration.
Mature AI asset management experience

At the technical level, it can solve the whole life cycle management and support of the entire business and model, and at the data application level which above the technical level, it meet the needs of enterprise knowledge precipitation and knowledge fusion.