You will get a machine learning model built and deployed with FastAPI
Project details
I will build a machine learning model for your use case and deploy it as a live FastAPI service, so you get a real API endpoint your application can call, not just a model file.
I train the model on your data, optimize it for accuracy, then wrap it in a FastAPI app with endpoints for both single and batch predictions. Everything is containerized with Docker and tracked with MLflow so experiments are logged and results are fully reproducible.
Trained and deployed models achieving up to 90.23% accuracy and 0.89 ROC-AUC on real e-commerce datasets — served via FastAPI with Docker Compose and DVC versioning.
I train the model on your data, optimize it for accuracy, then wrap it in a FastAPI app with endpoints for both single and batch predictions. Everything is containerized with Docker and tracked with MLflow so experiments are logged and results are fully reproducible.
Trained and deployed models achieving up to 90.23% accuracy and 0.89 ROC-AUC on real e-commerce datasets — served via FastAPI with Docker Compose and DVC versioning.
What's included
| Service Tiers |
Starter
$150
|
Standard
$280
|
Advanced
$450
|
|---|---|---|---|
| Delivery Time | 5 days | 7 days | 14 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 1 | 2 | 3 |
Number of Graphs/Charts | 3 | 5 | 7 |
Model Validation/Testing | - | - | - |
Model Documentation | - | - | - |
Data Source Connectivity | - | - | - |
Source Code | - | - | - |
About M Wajeeh
ML Engineer | Python | Predictive Modeling | Data Analytics
Islamabad, Pakistan - 3:41 am local time
I help startups, students, and small teams build practical machine learning and data analytics solutions, from cleaning messy datasets and training models to building dashboards and deploying ML APIs.
My strongest areas are machine learning model development, predictive modeling, data preprocessing, and turning raw data into useful business insights. I also have hands-on experience with FastAPI, Docker, MLflow, DVC, and basic MLOps workflows, so I can help make ML projects more structured, reproducible, and easier to use beyond a notebook.
What I can help you with:
🧠 Machine Learning & Predictive Modeling
- Classification and regression models using Scikit-learn, XGBoost, TensorFlow, and PyTorch
- Data cleaning, preprocessing, feature engineering, and model evaluation
- Model performance improvement using proper metrics, validation, and experimentation
- End-to-end ML workflows from raw data to trained model
🚀 ML Deployment & Workflow Automation
- FastAPI-based machine learning APIs
- Dockerized ML applications
- Experiment tracking with MLflow
- Data/model versioning with DVC
- Clean project structure, documentation, and reproducible code
📊 Data Analytics & Dashboards
- Power BI and Tableau dashboards
- SQL analysis using joins, CTEs, window functions, and subqueries
- Python-based EDA, data cleaning, and reporting
- Business-ready insights for non-technical stakeholders
🤖 Generative AI Exposure
I have also worked with beginner-to-intermediate Generative AI and RAG concepts using LangChain, ChromaDB, and LLM APIs. I can help with document-based Q&A prototypes, semantic search, and AI feature experimentation.
📌 Selected Projects:
- PurchasePulse: XGBoost purchase intent model with 90.23% accuracy and 0.89 ROC-AUC, served through FastAPI with Docker and DVC
- Telco ChurnGuard: Customer churn prediction pipeline with 85% accuracy, CI/CD using GitHub Actions, and batch prediction workflow
- RAG Insight Engine: Document-based Q&A prototype using LangChain and ChromaDB with citation support
- E-Commerce SQL Analysis: 99,000+ orders analyzed in BigQuery to extract customer and sales insights
- Power BI Sales Dashboard: 5-year retail analysis with product, regional, and revenue insights
🛠️ Core Stack:
Python | Scikit-learn | XGBoost | TensorFlow | PyTorch | FastAPI | Docker | MLflow | DVC | SQL | BigQuery | PostgreSQL | Power BI | Tableau | GitHub Actions | LangChain | ChromaDB
✅ Why work with me:
- Winner of Vyrothon 2026 AI Hackathon among 580+ applicants
- Oracle Certified Generative AI Professional
- AI graduate with hands-on ML, analytics, and deployment project experience
- Clear communication, clean documentation, and structured delivery
- I focus on practical solutions, not just experimental notebooks
Best fit projects:
- Machine learning model training
- Predictive analytics
- Data cleaning and EDA
- Power BI/Tableau dashboards
- ML API deployment with FastAPI
- Beginner-to-intermediate AI/GenAI prototypes
If your project involves machine learning, data analytics, or making an ML model usable in a real workflow, send me a message and I’ll honestly tell you how I can help.
Steps for completing your project
After purchasing the project, send requirements so M Wajeeh can start the project.
Delivery time starts when M Wajeeh receives requirements from you.
M Wajeeh works on your project following the steps below.
Revisions may occur after the delivery date.
Data review and problem scoping
I review your dataset, confirm the prediction target, check data quality, and define the exact model type and evaluation metrics we'll use before writing a single line of code.
Model training and experimentation
I train and tune the model using MLflow experiment tracking, testing multiple configurations and selecting the best performer based on accuracy, ROC-AUC, or your chosen metric.