Browse categories
Explore
Fiverr Pro
English
$
USD
I will deploy AWS sagemaker models build mlops pipelines fastapi inference API setup
AWS AI Cloud Engineer Bedrock, Python, RAG, DevOps, Terraform
I’m an AWS AI & Cloud Engineer specializing in AWS Bedrock, Python, RAG, AI agents, cloud architecture, and DevOps. I build production-ready AI and cloud solutions including LLM applications, RAG syst...
About this Gig
Need to deploy your machine learning model into production on AWS? I can help you build a reliable AWS SageMaker ML deployment, MLOps pipeline, FastAPI inference API tailored to your application
.
Services include Amazon SageMaker, ML model deployment, SageMaker endpoints, FastAPI, REST API, Docker, MLflow, CI/CD, model monitoring, CloudWatch, autoscaling, model versioning, inference optimization and AWS deployment.
I can help with:
- SageMaker model training deployment
- Real-time inference endpoints
- FastAPI ML API integration
- Docker containerization
- MLOps workflow automation
- CI/CD pipeline setup
- Model versioning monitoring
- CloudWatch logging
- Endpoint scaling optimization
- SageMaker JumpStart deployment
- Production ML architecture
Ideal for AI startups, SaaS platforms, data science teams, ML engineers, agencies and businesses moving machine learning models from notebooks into production.
Share your model, AWS requirements or existing ML workflow, and Ill help turn it into a production-ready deployment.
Message me before ordering so I can review your requirements and recommend the right setup.
My Portfolio
FAQ
Can you deploy my existing ML model to SageMaker?
Yes. I can deploy supported models to SageMaker, configure inference endpoints, test predictions and connect the model to your application.
Can you create a FastAPI API for my ML model?
Yes. I can build a FastAPI inference layer that communicates with your deployed SageMaker endpoint and provides API access to your application.
Can you build an MLOps pipeline?
Yes. Depending on your requirements, I can configure model deployment workflows, versioning, CI/CD, monitoring and automated production processes.
Can you work with Docker or MLflow?
Yes. Docker containerization and MLflow can be incorporated where they fit your model architecture and deployment workflow.
Can you optimize an existing SageMaker deployment?
Yes. I can review your existing architecture and help improve endpoint configuration, inference performance, monitoring, scalability and AWS resource usage.
