MLOps & Cloud AI Deployments (AWS/GCP)

Pro

Turyal Neeshat

Starting at

/hr

About this service

Summary

I help businesses deploy, scale, and manage AI/ML models efficiently on AWS and GCP. Whether you need a CI/CD pipeline, containerization with Docker & Kubernetes or cloud cost optimization, I make sure a production-ready AI infrastructure that is scalable and efficient.

FAQs

Which cloud platforms do you support?
I primarily work with AWS (SageMaker, Lambda, EC2, EKS) and GCP (Vertex AI, Cloud Run, GKE) but can also assist with Azure ML deployments.
Can you help with cost optimization?
Yes! I design cost-efficient and scalable cloud infrastructure to minimize expenses while maintaining high performance.
How long does deployment take?
A standard AI model deployment takes 1-3 weeks, including testing, optimization, and monitoring setup.

What's included

End-to-End AI Model Deployment
Deploy AI models on AWS SageMaker, GCP Vertex AI, Lambda, Cloud Run, and other cloud platforms for scalable inference.
MLOps Pipeline Setup
Build CI/CD pipelines using MLflow, Vertex Pipelines, SageMaker Pipelines, and Kubeflow for seamless model versioning and deployment.
Cloud Infrastructure Optimization
Implement auto-scaling, security best practices, and cost-saving strategies to optimize cloud resources efficiently.
Model Containerization & Orchestration
Use Docker, Kubernetes, Helm, and Terraform to package and deploy AI models with high availability and reliability.
API Development & Integration
Expose models through FastAPI, Flask, or gRPC for seamless integration with applications and business systems.
Monitoring & Logging
Set up Prometheus, Grafana, AWS CloudWatch, and GCP Stackdriver for real-time model performance monitoring and logging.

Skills and tools

DevOps Engineer

ML Engineer

AI Developer