You will get Deployment of your ML Model as a FastAPI Inference API (Docker-ready)
Project details
I will turn your trained ML model (currently in a notebook or script) into a clean, callable FastAPI inference API your app can use.
You’ll get:
• Prediction endpoint(s) with clear Pydantic request/response schemas
• Reliable model loading (joblib/pickle/ONNX) + preprocessing included (scalers/encoders)
• Interactive Swagger docs at /docs
• A clean repo with README + sample requests (curl/Postman)
• Optional Docker for reproducible runs
Best for: startups and teams that need a dependable model endpoint quickly, with a clear handoff and documentation.
To start, send your model file, feature list/order, preprocessing details, and 1–3 example inputs/outputs.
You’ll get:
• Prediction endpoint(s) with clear Pydantic request/response schemas
• Reliable model loading (joblib/pickle/ONNX) + preprocessing included (scalers/encoders)
• Interactive Swagger docs at /docs
• A clean repo with README + sample requests (curl/Postman)
• Optional Docker for reproducible runs
Best for: startups and teams that need a dependable model endpoint quickly, with a clear handoff and documentation.
To start, send your model file, feature list/order, preprocessing details, and 1–3 example inputs/outputs.
Machine Learning Tools
Amazon SageMaker, ChatGPT, Deeplearning4j, fastText, GitHub Copilot, Keras, MATLAB, MLflow, NumPy, OpenCV, pandas, Python, Python Scikit-Learn, PyTorch, scikit-learn, SciPy, Scrapy, SQL, Stanford CoreNLP, Tableau, TensorFlow, XGBoostWhat's included
| Service Tiers |
Starter
$149
|
Standard
$299
|
Advanced
$499
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 7 days |
Number of Revisions | 1 | 2 | 2 |
Number of Model Variations | 1 | 1 | 1 |
Number of Scenarios | 1 | 2 | 3 |
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.
Fast Delivery
+$75
Additional Revision
+$35
Additional Model Variation
(+ 2 Days)
+$120
Additional Scenario
(+ 1 Day)
+$60
Model Validation/Testing
(+ 1 Day)
+$50Frequently asked questions
About Ussama
Machine Learning Engineer
Talagang, Pakistan - 1:38 am local time
Deploy your ML model as a FastAPI inference API
If your model is currently in a notebook/script, I’ll turn it into a clean, callable service with:
• FastAPI endpoints (single + optional batch predictions)
• Pydantic schemas for clear request/response contracts
• Reliable model loading (joblib/pickle/ONNX) + versioned artifacts
• README + sample requests (curl/Postman)
• Optional: Docker for reproducible runs
➕ Add-on A (Optional): Data Cleaning + EDA (Pandas)
• Cleaning pipeline + sanity checks
• EDA notebook + practical insights
➕ Add-on B (Optional): Evaluation + Threshold Tuning Report
• Metrics (Accuracy/Precision/Recall/F1) + confusion matrix
• Threshold recommendation aligned with your goal
How I work: clear scope → fast delivery → clean documentation.
If you want your model running as an API your app can call, message me.
Steps for completing your project
After purchasing the project, send requirements so Ussama can start the project.
Delivery time starts when Ussama receives requirements from you.
Ussama works on your project following the steps below.
Revisions may occur after the delivery date.
Requirements & API spec
Confirm inputs/outputs, preprocessing, endpoint list, and success criteria.
Build FastAPI service
Implement endpoints, Pydantic schemas, model loading, and error handling.