You will get a scalable REST API for your ML model, built with FastAPI and Docker
Project details
You've trained a model that works in a notebook. This project turns it into a service your app, your team, or your customers can actually call.
I package your trained model behind a FastAPI application, containerize it with Docker, and hand back a running, documented API — not a script that only works on your laptop. I work with scikit-learn, XGBoost, PyTorch, TensorFlow, Keras, and Hugging Face models.
What makes this different: I validate that the deployed API returns predictions identical to your original model before delivery, and I confirm the endpoint schema with you in writing before writing any code. No scope surprises, no silent behavior changes between your notebook and production.
You get the full source code, a production Dockerfile, auto-generated Swagger documentation, and a README you can hand to an engineer. Nothing is locked behind my involvement — the repo is yours.
This is for data scientists who need to hand a model to engineering, startups shipping their first ML feature, and teams sitting on a .pkl file with no clear path to production.
I package your trained model behind a FastAPI application, containerize it with Docker, and hand back a running, documented API — not a script that only works on your laptop. I work with scikit-learn, XGBoost, PyTorch, TensorFlow, Keras, and Hugging Face models.
What makes this different: I validate that the deployed API returns predictions identical to your original model before delivery, and I confirm the endpoint schema with you in writing before writing any code. No scope surprises, no silent behavior changes between your notebook and production.
You get the full source code, a production Dockerfile, auto-generated Swagger documentation, and a README you can hand to an engineer. Nothing is locked behind my involvement — the repo is yours.
This is for data scientists who need to hand a model to engineering, startups shipping their first ML feature, and teams sitting on a .pkl file with no clear path to production.
Machine Learning Tools
Amazon SageMaker, Azure Machine Learning, Keras, Kubeflow, MLflow, NumPy, pandas, Python, Python Scikit-Learn, PyTorch, TensorFlow, Vertex AI, XGBoostWhat's included
| Service Tiers |
Starter
$120
|
Standard
$325
|
Advanced
$750
|
|---|---|---|---|
| Delivery Time | 3 days | 6 days | 12 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 1 | 3 |
Number of Scenarios | 1 | 3 | 6 |
Model Validation/Testing | - | ||
Model Documentation | |||
Data Source Connectivity | - | - | |
Source Code |
Optional add-ons
You can add these on the next page.
Additional Revision
+$40
Additional Model Variation
(+ 2 Days)
+$85
Model Validation/Testing
(+ 2 Days)
+$70
Data Source Connectivity
(+ 2 Days)
+$110
Cloud deployment setup
(+ 3 Days)
+$150
GPU inference optimization
(+ 4 Days)
+$200Frequently asked questions
About Mohammed
AI/ML Engineer | Agents, MCP Servers & ML Deployment
Benslimane, Morocco - 3:32 pm local time
What I build
• Agent infrastructure — tool-calling agents, orchestration, state and memory, error recovery, cost and token controls
• MCP servers — custom Model Context Protocol servers connecting Claude and other LLMs to your APIs, databases, and internal tools
• Model deployment — FastAPI services, Docker containers, batch and real-time inference endpoints
• MLOps — CI/CD pipelines, model versioning, monitoring, automated retraining
• Model training — scikit-learn, XGBoost, PyTorch, TensorFlow, fine-tuning
How I work
I confirm scope and interfaces with you in writing before writing code, and I verify behavior matches spec before delivery. You get full source code, documentation, and a walkthrough — nothing locked behind my involvement.
I'm early on Upwork and pricing accordingly. You get more attention per project than from someone juggling ten contracts, and my work is public on GitHub so you can judge the code before hiring rather than after.
Tell me what you're trying to build and I'll tell you honestly whether I'm the right fit.
Steps for completing your project
After purchasing the project, send requirements so Mohammed can start the project.
Delivery time starts when Mohammed receives requirements from you.
Mohammed works on your project following the steps below.
Revisions may occur after the delivery date.
Requirements review and scope confirmation
I review your model, sample inputs, and preprocessing code, then confirm the endpoints, input/output schema, and deployment target with you in writing before any code is written.
API development and containerization
I build the FastAPI service with your endpoints and Pydantic validation, then package it in a multi-stage Docker image with pinned dependencies so it runs identically on any machine.