You will get Deploy ML Model on Kubernetes with Autoscaling
Project details
I help teams deploy trained machine learning models as scalable, production-ready Kubernetes inference services.
This project focuses strictly on ML inference deployment, autoscaling, reliability, and monitoring — not model training or data science experimentation.
Your model is packaged into a Docker container and deployed on Kubernetes with proper health checks, autoscaling (HPA), and rollout safety to handle real-world traffic.
This service is ideal for startups and engineering teams that already have a trained model and need it running reliably in production, with clean infrastructure and predictable costs.
This project focuses strictly on ML inference deployment, autoscaling, reliability, and monitoring — not model training or data science experimentation.
Your model is packaged into a Docker container and deployed on Kubernetes with proper health checks, autoscaling (HPA), and rollout safety to handle real-world traffic.
This service is ideal for startups and engineering teams that already have a trained model and need it running reliably in production, with clean infrastructure and predictable costs.
Machine Learning Tools
Amazon SageMaker, GitHub Copilot, Kubeflow, MLflow, PythonWhat's included
| Service Tiers |
Starter
$199
|
Standard
$399
|
Advanced
$599
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 5 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 - 1:57 pm 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 inference requirements and model artifact
Review the trained ML model, inference requirements, Kubernetes environment details, and expected traffic to confirm scope and deployment approach.
Containerization and Kubernetes setup
Package the model into a Docker container and configure Kubernetes Deployment, Service, and autoscaling (HPA) based on the selected service tier.