You will get ML Model Deployed to AWS with SageMaker, CI/CD, and API Microservice
Project details
This project delivers a secure, production-ready deployment of your trained machine learning model as an AWS-hosted microservice. I use AWS SageMaker for scalable inference and optionally integrate API Gateway, Lambda, and CI/CD pipelines using AWS CDK, CloudFormation, or GitHub. You choose your preferred delivery method—whether you want the infrastructure as code, GitHub integration with auto-deploy, or a direct deployment into your AWS account.
The Starter tier includes a working SageMaker deployment and optional API interface. The Standard tier adds version control using the SageMaker Model Registry and a basic CI/CD pipeline. The Advanced tier offers end-to-end infrastructure with monitoring, logging (CloudWatch), data visualization (QuickSight), model analytics, and full documentation.
Optional add-ons include A/B testing, canary deployments, and shadow testing frameworks. If you don’t yet have a model, I offer a separate service to train one based on your use case.
The Starter tier includes a working SageMaker deployment and optional API interface. The Standard tier adds version control using the SageMaker Model Registry and a basic CI/CD pipeline. The Advanced tier offers end-to-end infrastructure with monitoring, logging (CloudWatch), data visualization (QuickSight), model analytics, and full documentation.
Optional add-ons include A/B testing, canary deployments, and shadow testing frameworks. If you don’t yet have a model, I offer a separate service to train one based on your use case.
Machine Learning Tools
Amazon SageMaker, MLflow, NumPy, pandas, Python, Python Scikit-Learn, PyTorch, SQL, TensorFlowWhat's included
| Service Tiers |
Starter
$300
|
Standard
$500
|
Advanced
$850
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 8 days |
Number of Revisions | 1 | 3 | 3 |
Number of Model Variations | 1 | 1 | 2 |
Number of Scenarios | 1 | 1 | 1 |
Number of Graphs/Charts | 1 | 1 | 2 |
Model Validation/Testing | - | - | - |
Model Documentation | - | - | |
Data Source Connectivity | - | - | |
Source Code |
Optional add-ons
You can add these on the next page.
Model Documentation
(+ 1 Day)
+$100
Data Source Connectivity
(+ 2 Days)
+$150
A/B Testing Framework
(+ 2 Days)
+$200
Canary Deployment
(+ 2 Days)
+$200
Shadow Testing
(+ 2 Days)
+$200Frequently asked questions
About Adrian
AWS Cloud Architecture & Machine Learning Solutions
Moravia, Costa Rica - 9:59 am local time
I design and deploy AWS systems with a focus on cost optimization and long-term scalability for growing teams. My solutions deliver ready-to-go infrastructure using modern DevOps practices, secure network design, and powerful AI systems customized to your needs.
🔹 My services provide a full deployment lifecycle🔹
↳ Your Business Needs → Architecture design → Infrastructure Code → Deployment → Ongoing Support
This applies whether you're starting from scratch or integrating with an existing setup.
🔹 What you get when working with me 🔹
• Application code — direct development into your application or infrastructure code with comments and setup instructions (CDK, CloudFormation, Terraform, and Python (Boto3), JAVA)
• System documentation — architecture diagrams and design notes to support team onboarding
• Communication — I work closely with you to align on your goals and make adjustments as needed throughout the process
• Ongoing support — Available for bug fixes, future updates, or onboarding new engineers
✨ I'm open to one-time projects or building something long-term together. Reach out if you'd like to set up an intro call and talk through your goals.
Steps for completing your project
After purchasing the project, send requirements so Adrian can start the project.
Delivery time starts when Adrian receives requirements from you.
Adrian works on your project following the steps below.
Revisions may occur after the delivery date.
Review & Confirm Requirements
Review model file, framework, and delivery preferences. Confirm scope, IAM access, and any requirements. You’ll receive a detailed infrastructure diagram and documentation overview.
Deployment & Configuration
Deploy the model using CDK, CloudFormation, GitHub, or directly in AWS. Set up SageMaker, Lambda, API Gateway, and CI/CD if needed. Add-ons like A/B testing or canary rollout are integrated here.