Transforming your business through AI-powered solutions in the cloud with a systematic approach
Gather business and technical requirements.
Define project scope and technical stack, aligning business goals with AI capabilities.
Identify and design use cases for AI, including Retrieval-Augmented Generation (RAG) systems.
Design architecture for scalability, security, and modularity to ensure the system grows as needed.
Select suitable AWS services like S3, SageMaker, and Lambda to power AI solutions.
Prepare and ingest data from multiple sources to feed into AI models and pipelines.
Implement retrieval layers such as OpenSearch for context storage to enhance search accuracy.
Develop and integrate models with Retrieval-Augmented Generation (RAG) systems.
Automate deployments using tools like CodePipeline and CodeBuild for continuous delivery.
Train or fine-tune AI models using SageMaker and optimize them for better performance.
Set up automated pipelines to ensure data freshness using AWS Glue, DMS, and Kinesis.
Implement auto-scaling to adapt to demand and monitor performance in real-time.
Deploy and automate AI solutions on AWS for scalability and efficient resource management.
Package AI models and retrieval systems for deployment, ensuring efficient integration.
Collect feedback and performance data to iterate and improve the AI model and system.
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