Cloud AI Automation Development

AWS AI Automation Development Workflow

Requirements & Solution Design

• Gather business and technical requirements

• Identify use cases for AI (including RAG systems)

• Select suitable AWS services (S3, SageMaker, OpenSearch, Lambda, etc.)

• Design architecture for scalability, security, and modularity

  Data Preparation & Ingestion

• Collect, clean, and preprocess data

• Store data securely in S3 or appropriate data lakes

• Set up pipelines to keep data fresh (AWS Glue, DMS, Kinesis)

  Model Development & RAG Integration

• Train or fine-tune AI models (LLMs on SageMaker)

• Build retrieval layer (e.g., OpenSearch or RDS for context storage)

• Implement Retrieval-Augmented Generation workflows that combine retrieved context + generative responses

• Evaluate and iterate

  Deployment & Automation on AWS

• Package models and retrieval systems for deployment

• Use SageMaker endpoints / Lambda functions for serving

• Automate deployments with CI/CD pipelines (CodePipeline, CodeBuild)

• Configure infrastructure as code (CloudFormation, Terraform)

  Real-Time Monitoring & Adaptation

• Set up logging and monitoring (CloudWatch, X-Ray)

• Use alarms and dashboards to track latency, throughput, error rates

• Implement auto-scaling to adapt to demand

• Enable periodic retraining or hot swaps to keep models accurate

  Delivery of Insights & Continuous Improvement

• Feed outputs to end applications (chatbots, dashboards, APIs)

• Collect feedback & performance data

• Iterate to improve models, retrieval, and overall workflows