Which AWS services does this skill support?

It specializes in Amazon Bedrock, SageMaker, and AI services like Textract and Rekognition, alongside infrastructure tools like CDK, Lambda, and Step Functions.

Does this skill provide deployable code?

Yes, every engagement aims to produce working code and CDK templates that can be deployed directly to your AWS environment for rapid validation.

What is an AWS Forward-Deployed Engineer (FDE)?

An FDE is a builder-first technical partner who creates working prototypes and tangible artifacts rather than just providing high-level advisory or documentation.

How does this skill help with AWS cost management?

The skill provides immediate cost analysis for both POC and production environments, identifying optimization levers to ensure cost-effective AI/ML scaling.

AWS FDE for AI/ML

Name: AWS FDE for AI/ML
Author: dgallitelli

bydgallitelli

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Ciencia de Datos y ML

Builds production-ready AI/ML prototypes and POCs on AWS with cost optimization and Well-Architected best practices.

The AWS Forward-Deployed Engineer (FDE) for AI/ML skill transforms Claude into a hands-on technical partner that prioritizes building over advisory. It enables the rapid creation of working prototypes using services like Amazon Bedrock and SageMaker, delivering not just code but also Infrastructure as Code (CDK), detailed cost analysis, and evolution roadmaps. This skill is ideal for teams needing to validate AI/ML concepts quickly while ensuring they align with AWS Well-Architected pillars and cost-efficiency standards from day one.

Características Principales

01Rapid prototyping of GenAI and ML solutions on AWS

02Well-Architected Framework reviews for AI/ML workloads

03Integrated AWS cost estimation and production scaling projections

041 GitHub stars

05Automated CDK and CloudFormation template generation

06End-to-end delivery of working POCs and technical artifacts

Casos de Uso

01Performing architectural reviews and cost optimization for existing AWS AI services

02Building a Generative AI RAG prototype using Amazon Bedrock and OpenSearch

03Designing and deploying scalable ML pipelines with SageMaker and AWS Lambda

Características Principales

01Rapid prototyping of GenAI and ML solutions on AWS

02Well-Architected Framework reviews for AI/ML workloads

03Integrated AWS cost estimation and production scaling projections

041 GitHub stars

05Automated CDK and CloudFormation template generation

06End-to-end delivery of working POCs and technical artifacts

Casos de Uso

01Performing architectural reviews and cost optimization for existing AWS AI services

02Building a Generative AI RAG prototype using Amazon Bedrock and OpenSearch

03Designing and deploying scalable ML pipelines with SageMaker and AWS Lambda