You will get a trained, tuned, and evaluated machine learning model
Project details
I will develop a machine learning model for your classification or regression problem using the dataset you provide.
Depending on the selected package, the work can include data preparation, feature preprocessing, baseline and candidate model training, model comparison, hyperparameter tuning, validation, error analysis, explainability, and reusable inference artifacts.
You will receive the trained model, source code, evaluation results, and documentation included in your package. The model will be assessed using suitable metrics based on the prediction problem and dataset characteristics.
This service is best suited for structured datasets provided in CSV, Excel, or Parquet format. A specific accuracy or business outcome cannot be guaranteed because model performance depends on data quality, sample size, feature relevance, target definition, and class balance.
Large-scale deep learning, manual data labeling, cloud compute costs, production deployment, ongoing retraining, and continuous monitoring are not included unless agreed separately.
Depending on the selected package, the work can include data preparation, feature preprocessing, baseline and candidate model training, model comparison, hyperparameter tuning, validation, error analysis, explainability, and reusable inference artifacts.
You will receive the trained model, source code, evaluation results, and documentation included in your package. The model will be assessed using suitable metrics based on the prediction problem and dataset characteristics.
This service is best suited for structured datasets provided in CSV, Excel, or Parquet format. A specific accuracy or business outcome cannot be guaranteed because model performance depends on data quality, sample size, feature relevance, target definition, and class balance.
Large-scale deep learning, manual data labeling, cloud compute costs, production deployment, ongoing retraining, and continuous monitoring are not included unless agreed separately.
Machine Learning Tools
MLflow, NumPy, pandas, Python, scikit-learn, SciPy, XGBoostWhat's included
| Service Tiers |
Starter
$80
|
Standard
$180
|
Advanced
$350
|
|---|---|---|---|
| Delivery Time | 4 days | 7 days | 10 days |
Number of Revisions | 1 | 2 | 2 |
Number of Model Variations | 1 | 3 | 4 |
Number of Scenarios | 1 | 1 | 1 |
Number of Graphs/Charts | 2 | 5 | 8 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | - | - | - |
Source Code |
Optional add-ons
You can add these on the next page.
Additional Revision
+$25
Additional Model Variation
(+ 2 Days)
+$40
Additional Graph/Chart
(+ 1 Day)
+$15Frequently asked questions
About Chathuranga
AI/ML Engineer | RAG, AI Agents, FastAPI, MLOps & AWS
Nugegoda, Sri Lanka - 7:35 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 dataset and project goal
I will inspect the dataset structure, target, feature types, data quality, and requirements before confirming the modeling approach.
Prepare the data for modeling
I will prepare the data according to the selected package, including suitable cleaning, feature handling, and train-validation-test splitting.