Combining Microsoft and Azure OpenAI to build an enterprise-grade, trusted AI ecosystem
Azure OpenAI vs OpenAI
| Feature | OpenAI | Azure OpenAI |
|---|---|---|
| Security and Data Privacy | Basic security | Enterprise-grade security, RBAC, customer-managed keys |
| Compliance | Not currently available | SOC2, ISO, HIPAA, CSA STAR |
| Reliability | No SLA currently available | Azure SLA |
| Responsible AI | Separate Safety Classifier (adds latency) | Built-in, enterprise-grade, low latency moderation and harm prevention |
| APIs | REST APIs + Python SDK | REST APIs + Python, C#, etc. SDKs |
If you are facing the following needs, we can help immediately
I need a general-purpose model that can handle multiple tasks e.g. Translation, Entity Recognition, Sentiment Analysis
I need to generate human-like content while ensuring data privacy and security e.g. Summarization, Content Expansion, Rewriting, Code
I need rapid prototyping and fast time-to-market to address many use cases
Language models using little or no training data
Hybrid solutions combining the above use cases
The Best Path for Enterprise AI Implementation
Power 365, GitHub Copilot, Office Copilot enterprise deployment and access control
Secure querying of private data to improve customer service/business efficiency
Integration of CI/CD, Infrastructure as Code, code quality improvement
AI Success Story — Gaming Industry
#AIDevelopment #CustomerServiceBot #GitHubCopilot #AzureMigration
Assisting a renowned gaming group in implementing Azure OpenAI and GitHub Copilot
Establishing dedicated customer service AI and development automation processes to improve code quality and time-to-market
Project Implementation Experience
Leveraging years of system integration experience, our company has successfully led the construction of AI computing centers, with core focus on deep integration of HPC computing resources and ultra-high-speed networks. Through the deployment of InfiniBand low-latency architecture, we optimize RDMA communication efficiency between GPU clusters, effectively eliminating data transmission bottlenecks during large-scale training.
Combined with non-blocking fabric technology, we ensure computing nodes demonstrate extreme linear scalability, creating an efficient and stable underlying architecture for AI model development.
Facing the thermal challenges brought by high computing power, we simultaneously introduced DLC liquid cooling technology. This not only solves the heat dissipation challenges of high-wattage single cabinets but also significantly reduces the PUE value of computing centers, achieving energy-saving goals while improving computing stability.
Project Implementation Achievements