Cloud AI Development And Deployment
Cloud AI Development And Deployment
Transforming your business through AI-powered solutions in the cloud with a systematic approach
AWS AI Automation Development Workflow
Gather business and technical requirements.
Requirements & Solution Design
Define project scope and technical stack, aligning business goals with AI capabilities.
Identify Use Cases for AI
Identify and design use cases for AI, including Retrieval-Augmented Generation (RAG) systems.
Design Architecture for Scalability
Design architecture for scalability, security, and modularity to ensure the system grows as needed.
Select Suitable AWS Services
Select suitable AWS services like S3, SageMaker, and Lambda to power AI solutions.
Data Preparation & Ingestion
Prepare and ingest data from multiple sources to feed into AI models and pipelines.
Build Retrieval Layer
Implement retrieval layers such as OpenSearch for context storage to enhance search accuracy.
Model Development & Integration
Develop and integrate models with Retrieval-Augmented Generation (RAG) systems.
Automate Deployments with CI/CD Pipelines
Automate deployments using tools like CodePipeline and CodeBuild for continuous delivery.
Train or Fine-Tune AI Models
Train or fine-tune AI models using SageMaker and optimize them for better performance.
Set Up Pipelines for Data Freshness
Set up automated pipelines to ensure data freshness using AWS Glue, DMS, and Kinesis.
Implement Auto-Scaling & Monitoring
Implement auto-scaling to adapt to demand and monitor performance in real-time.
Deploy & Automate on AWS
Deploy and automate AI solutions on AWS for scalability and efficient resource management.
Package Models & Retrieval Systems for Deployment
Package AI models and retrieval systems for deployment, ensuring efficient integration.
Collect Feedback & Performance Data
Collect feedback and performance data to iterate and improve the AI model and system.
Client Testimonials
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