ML-Flow

ML-Flow Notebook

An interactive Jupyter Notebook that demonstrates how to leverage MLflow for experiment tracking, model logging, and lifecycle management in an end-to-end machine learning workflow.

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Table of Contents

1. Project Overview
2. Contents of the Notebook
3. Getting Started
4. Usage
5. MLflow Features Used
6. Technologies & Dependencies
7. Authors & Contact
8. License

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Project Overview

This notebook provides a step-by-step guide for building, tracking, and managing a machine learning model using MLflow. You'll learn to:

• Train a model on a dataset
• Log parameters, metrics, and artifacts
• Visualize experiment runs using the MLflow Tracking UI
• Package and optionally register models for deployment

It’s designed to be a practical reference for experimenting with MLflow in research or production settings.

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Contents of the Notebook

The notebook is structured into the following sections:

1. Setup & Imports
   Installing necessary libraries and configuring MLflow.

2. Data Loading & Preprocessing
   Importing datasets and preparing features.

3. Model Training & Evaluation
   Training a model (e.g., regression or classification), evaluating accuracy, logging run metadata.

4. MLflow Logging
   Using mlflow.start_run() to log:

   • Parameters (e.g., hyperparameters)
   • Metrics (e.g., accuracy, RMSE)
   • Artifacts (e.g., plots, model files)
   • Model in MLflow model format for later inference or deployment

5. MLflow UI
   How to launch and use mlflow ui to compare runs and inspect logged data.

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Getting Started

1. Clone the repository

   git clone https://github.com/monika393/ML-Flow.git
   cd ML-Flow