AI implementation and deployment service]

[Relying on AWS full-stack AI infrastructure such as Amazon Bedrock and Amazon SageMaker, Taihu Huiyun provides enterprises with end-to-end implementation and deployment services ranging from large model selection, architecture design, model fine-tuning, application integration to production operation and maintenance, covering the full lifecycle implementation of generative AI, machine learning, and agent applications. The AI migration and deployment engine is deeply integrated with automated evaluation, intelligent orchestration, and FinOps cost optimization capabilities, helping customers quickly build a high-performance, secure, compliant, and continuously evolving AI application system on Amazon. Large model selection and evaluation: Based on mainstream large models supported by Amazon Bedrock, we quantitatively compare them from dimensions such as inference capability, response latency, Token cost, and Chinese support, and output a selection recommendation report. We also evaluate the model deployment method in conjunction with industry compliance requirements to ensure that data does not leave the domain and audit traceability is maintained. Cloud architecture design: Designing AI application architecture based on the AWS Well-Architected framework: Integrating Bedrock RAG architecture with Kendra/OpenSearch; SageMaker MLOps full-chain architecture; Bedrock Agents intelligent agent architecture; Integrating AWS IAM, KMS, VPC, CloudTrail, and GuardDuty to build an enterprise-level security defense line. Model fine-tuning and optimization: Assist clients in completing data cleaning, annotation, and desensitization; choose full-parameter fine-tuning or parameter-efficient fine-tuning (LoRA/QLoRA); utilize Bedrock's fine-tuning capabilities to customize exclusive models; build an automated evaluation pipeline. Application integration and development implementation: Encapsulate AI inference capabilities into API interfaces based on AWS Lambda, API Gateway, and EKS/ECS; support integration with mainstream enterprise systems such as CRM, ERP, and collaborative office tools; develop front-end interactive interfaces based on AWS Amplify or containerized deployment. Production operation and maintenance and continuous optimization: Build a multi-dimensional monitoring system based on CloudWatch and Grafana; continuously monitor model performance degradation (Model Drift); continuously optimize costs based on Cost Explorer and Savings Plans; achieve automated response to security threats based on GuardDuty and Macie.