You will get a FastAPI API for your existing machine learning model
Project details
I will convert your existing trained machine learning model into a clean and usable FastAPI prediction API. The API will load your model and preprocessing artifacts, validate incoming requests, return structured prediction responses, and include a health-check endpoint.
Depending on the selected package, I can also provide Docker support, automated API tests, improved error handling, data-source connectivity, and support for additional model variants.
You will receive organized source code, setup instructions, API usage examples, and clear documentation. This service is intended for clients who already have a trained model and need a reliable API layer for integration, testing, or deployment.
Before starting, I will review the supplied model files, preprocessing requirements, expected inputs and outputs, and integration needs to confirm compatibility and scope.
Depending on the selected package, I can also provide Docker support, automated API tests, improved error handling, data-source connectivity, and support for additional model variants.
You will receive organized source code, setup instructions, API usage examples, and clear documentation. This service is intended for clients who already have a trained model and need a reliable API layer for integration, testing, or deployment.
Before starting, I will review the supplied model files, preprocessing requirements, expected inputs and outputs, and integration needs to confirm compatibility and scope.
Machine Learning Tools
Keras, NumPy, Open Neural Network Exchange, pandas, Python, PyTorch, scikit-learn, SciPy, TensorFlow, XGBoostWhat's included
| Service Tiers |
Starter
$50
|
Standard
$125
|
Advanced
$250
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 7 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 1 | 2 |
Number of Scenarios | 1 | 2 | 3 |
Model Validation/Testing | - | ||
Model Documentation | |||
Data Source Connectivity | - | - | |
Source Code |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$20 - $50
Additional Revision
+$20
Additional Model Variation
(+ 2 Days)
+$50Frequently asked questions
About Chathuranga
AI/ML Engineer | RAG, AI Agents, FastAPI, MLOps & AWS
Nugegoda, Sri Lanka - 1:42 am local time
Need a RAG application, AI agent, machine learning API, or help improving an existing AI project? I help businesses turn AI ideas, datasets, and prototypes into tested, maintainable, and deployment-ready systems.
I can support you with:
• RAG and LLM applications using LangChain, LangGraph, OpenAI, Hugging Face, and vector databases
• AI agents with tools, memory, conditional workflows, structured outputs, and human-in-the-loop approval
• Machine learning models using scikit-learn, XGBoost, LightGBM, CatBoost, TensorFlow, and PyTorch
• FastAPI model services, backend integrations, and REST APIs
• Docker, automated testing, CI/CD, monitoring, model versioning, and AWS deployment
• Debugging, improving, and productionizing existing AI/ML projects
My project experience includes enterprise fraud detection, RAG-driven decision intelligence, multimodal recommendation, NLP inference APIs, persistent tool agents, and cloud-native AI/ML deployment.
You will receive maintainable code, clear documentation, honest technical communication, and an implementation aligned with your business requirements.
Steps for completing your project
After purchasing the project, send requirements so Chathuranga can start the project.
Delivery time starts when Chathuranga receives requirements from you.
Chathuranga works on your project following the steps below.
Revisions may occur after the delivery date.
Review the model and requirements
I will inspect the supplied model, preprocessing artifacts, sample inputs, and API requirements to confirm compatibility and define the implementation scope.
Build the prediction API
I will create the FastAPI application, load the model and preprocessing artifacts, and implement validated prediction and health-check endpoints.