Customer Satisfaction prediction with MLOps

For Demo Please visit: https://youtu.be/WYPCLE6CHS8

Overview

CustSatMLOps is a project aimed at predicting customer satisfaction using machine learning algorithms. It integrates MLOps practices using ZenML and MLflow for streamlined deployment and management.

Features

• Data Processing: Includes scripts for data cleaning and preprocessing.
• Model Training: Implements various ML algorithms for training models.
• MLOps Integration: Utilizes ZenML for pipeline orchestration and MLflow for experiment tracking and model management.
• Deployment: Provides tools for deploying models into production.

Pipeline structure

Installation

1. Clone the repository:

   git clone https://github.com/mirsaidl/CustSatMLOps.git

2. Navigate to the project directory:

   cd CustSatMLOps

3. Install the required packages:

   pip install -r requirements.txt

4. Starting with ZenML 0.20.0, ZenML comes bundled with a React-based dashboard. This dashboard allows you to observe your stacks, stack components and pipeline DAGs in a dashboard interface. To access this, you need to launch the ZenML Server and Dashboard locally, but first you must install the optional dependencies for the ZenML server:

pip install zenml["server"]
zenml up

If you are running the run_deployment.py script, you will also need to install some integrations using ZenML:

zenml integration install mlflow -y

The project can only be executed with a ZenML stack that has an MLflow experiment tracker and model deployer as a component. Configuring a new stack with the two components are as follows:

zenml integration install mlflow -y
zenml experiment-tracker register mlflow_tracker --flavor=mlflow
zenml model-deployer register mlflow --flavor=mlflow
zenml stack register mlflow_stack -a default -o default -d mlflow -e mlflow_tracker --set

Usage

1. Run the pipeline:

   python run_pipeline.py

2. Deploy the model:

   python run_deployment.py --config deploy
   python run_deployment.py --config predict

3. Run Streamlit on local:

   streamlit run app.py

Repository Structure

• data/: Contains the dataset.
• pipelines/: Defines the ML pipelines.
• steps/: Contains steps for data processing, training, and evaluation.
• src/: Source code for the project.
• requirements.txt: List of dependencies.

Acknowledgements

This project utilizes ZenML and MLflow for implementing MLOps.