You will get Set Up Production-Ready MLOps Pipeline on AWS
Project details
I help teams deploy trained machine learning models into reliable production systems using proven MLOps practices on AWS.
This project focuses on model deployment, scalability, monitoring, and operational stability — not model training or data science experimentation.
Using Docker, Kubernetes or Amazon SageMaker, CI/CD pipelines, and optional MLflow integration, I ensure your model is production-ready, scalable, and easy to maintain.
This service is ideal for startups and engineering teams who have a trained model but need it live, monitored, and running reliably in production with clean architecture and controlled costs.
This project focuses on model deployment, scalability, monitoring, and operational stability — not model training or data science experimentation.
Using Docker, Kubernetes or Amazon SageMaker, CI/CD pipelines, and optional MLflow integration, I ensure your model is production-ready, scalable, and easy to maintain.
This service is ideal for startups and engineering teams who have a trained model but need it live, monitored, and running reliably in production with clean architecture and controlled costs.
Machine Learning Tools
Amazon SageMaker, Databricks MLflow, GitHub Copilot, KubeflowWhat's included
| Service Tiers |
Starter
$155
|
Standard
$450
|
Advanced
$850
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 7 days |
Number of Revisions | Unlimited | Unlimited | Unlimited |
Number of Model Variations | 1 | 1 | 2 |
Number of Scenarios | 1 | 2 | 3 |
Number of Graphs/Charts | 0 | 0 | 0 |
Model Validation/Testing | |||
Model Documentation | - | ||
Data Source Connectivity | - | - | |
Source Code |
About Nitin
AWS DevOps Engineer | Terraform | Kubernetes | CI/CD | Cloud Security
Mumbai, India - 10:37 am local time
I help companies build reliable AWS infrastructure, automate deployments, and secure Kubernetes environments.
What I can help you with:
✔ AWS Infrastructure Architecture (EC2, VPC, IAM, Auto Scaling, EKS)
✔ Infrastructure as Code using Terraform / Ansible
✔ CI/CD Pipelines (Jenkins, GitLab CI, GitHub Actions)
✔ Kubernetes Deployment & Security Hardening
✔ DevSecOps – Container security scanning, compliance automation
✔ Monitoring & Observability (Grafana, CloudWatch)
Recent results:
• Reduced cloud infrastructure costs by 15–20%
• Implemented production-grade Kubernetes platforms
• Built automated CI/CD pipelines with zero-downtime deployments
If you need a reliable DevOps engineer to design, secure, or automate your cloud infrastructure, I’d be happy to help.
Steps for completing your project
After purchasing the project, send requirements so Nitin can start the project.
Delivery time starts when Nitin receives requirements from you.
Nitin works on your project following the steps below.
Revisions may occur after the delivery date.
Review client requirements, trained model, and deployment preferences.
Review the provided trained model, deployment requirements, and preferred platform (Kubernetes or Amazon SageMaker). Confirm AWS access, scope, and success criteria before starting implementation.
Architecture setup and containerization
Design the production deployment architecture. Containerize the model using Docker and configure infrastructure, environment variables, and deployment settings based on the selected service tier.