Developing and training a machine learning model is only half the journey. The real impact comes when your model steps out of the local Python environment and starts interacting with the real world. By deploying it as an API, you enable seamless integration with web applications, dashboards, or even mobile apps — transforming your standalone model into a practical, usable solution.
Check out my step-by-step guide to make your ML model fully functional and an integral part of the application development workflow.
Medium Blog:
Hosting your Machine Learning Model with Flask and Docker | by Gradient Thoughts | Oct, 2025 | Medium
A hands-on guide to turn your Machine Learning Model into a Web API
medium.com
P.S.
You are highly encouraged to check out the GitHub repository as well, containing all the required files and scripts.
Pranav-Bhatlapenumarthi
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Deploy_ML
Designed and deployed a production-ready Flask API hosting a trained Machine Learning model on Render. Containerized the application using Docker for environment consistency and easy deployment.
Deploy_ML
A web-based Machine Learning API built with Flask that serves real-time predictions from a trained model. The API is containerised with Docker and deployed on Render, making it accessible for integration with frontend applications or other services.
For a step-by-step description of the application, check out this hands-on demonstration: https://medium.com/@hello.gradientthoughts/building-and-hosting-a-machine-learning-model-with-flask-and-docker-1cd4f89cf256
Features
- Serve ML predictions: Host a trained model and provide predictions via a RESTful API.
- JSON-based endpoints: Accepts feature inputs in JSON format.
- Error handling: Returns informative error messages for invalid requests.
- CORS enabled: Supports frontend integration from other domains.
- Dockerized: Ensures environment consistency and easy deployment.
Project Structure
Deploy_ML/
│
├── app/
│ ├── main.py # Flask application
│ ├── model.py # Model loading and prediction
│
├── models/
│ └── model.joblib # Trained ML model
│
├── src/
│ └── main_model.py # Script for data preprocessing and model training
|
├── f1_dnf.csv # Dataset to train
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