https://github.com/prsdm/mlops-project

https://github.com/prsdm/mlops-project

ai awsecs data-science docker dvc evidentlyai fastapi machine-learning machinelearning mlflow mlops mlops-project mlops-workflow

Last synced: 6 months ago
JSON representation

• Host: GitHub
• URL: https://github.com/prsdm/mlops-project
• Owner: prsdm
• License: apache-2.0
• Created: 2024-08-18T13:35:20.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-11-18T11:46:18.000Z (11 months ago)
• Last Synced: 2025-03-29T12:02:38.593Z (6 months ago)
• Topics: ai, awsecs, data-science, docker, dvc, evidentlyai, fastapi, machine-learning, machinelearning, mlflow, mlops, mlops-project, mlops-workflow
• Language: HTML
• Homepage: https://prsdm.github.io/mlops-project/
• Size: 9.38 MB
• Stars: 119
• Watchers: 1
• Forks: 58
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • Funding: .github/FUNDING.yml
  • License: LICENSE

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README

          
# Insurance Cross Sell Prediction 🏠🏥

[![GitHub](https://img.shields.io/badge/GitHub-code-blue?style=flat&logo=github&logoColor=white&color=red)](https://github.com/prsdm/mlops-project) [![Medium](https://img.shields.io/badge/Medium-view_article-green?style=flat&logo=medium&logoColor=white&color=green)](https://medium.com/@prasadmahamulkar/machine-learning-operations-mlops-for-beginners-a5686bfe02b2)

Welcome to the Insurance Cross-Selling Prediction project! The goal of this project is to predict which customers are most likely to purchase additional insurance products using a machine learning model.

## Diagram

Below is the architecture diagram that illustrates the flow of the project from data ingestion to model deployment:

![Image](docs/mlops.jpg)

## Get Started

To get started with the project, follow the steps below:

#### 1. Clone the Repository

Clone the project repository from GitHub:

```bash

git clone https://github.com/prsdm/ml-project.git

```

```bash

cd ml-project

```

#### 2. Set Up the Environment

Ensure you have Python 3.8+ installed. Create a virtual environment and install the necessary dependencies:

```bash

python -m venv venv

source venv/bin/activate  # On Windows: venv\Scripts\activate

pip install -r requirements.txt

```

Alternatively, you can use the Makefile command:

```bash

make setup

```

#### 3. Data Preparation

Pull the data from DVC. If this command doesn't work, the train and test data are already present in the data folder:

```bash

dvc pull

```

#### 4. Train the Model

To train the model, run the following command:

```bash

python main.py 

```

Or use the Makefile command:

```bash

make run

```

This script will load the data, preprocess it, train the model, and save the trained model to the models/ directory.

#### 5. FastAPI

Start the FastAPI application by running:

```bash

uvicorn app:app --reload

```

#### 6. Docker

To build the Docker image and run the container:

```bash

docker build -t my_fastapi .

```

```bash

docker run -p 80:80 my_fastapi

```

Once your Docker image is built, you can push it to Docker Hub, making it accessible for deployment on any cloud platform.

#### 7. Monitor the Model

Integrate Evidently AI to monitor the model for data drift and performance degradation:

```bash

run monitor.ipynb file

```

## License

Copyright © 2024, [Prasad Mahamulkar](https://github.com/prsdm).

Released under the [Apache-2.0 license](LICENSE).