Weather Forecasting with MLOps: DVC, Airflow, and MLFlow 🌦️🚀

This repository demonstrates a complete end-to-end MLOps pipeline for building a weather forecasting model. By leveraging tools like DVC, Airflow, and MLFlow, we’ve created an automated, scalable, and reproducible workflow for collecting data, training models, and monitoring performance.

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Table of Contents

1. Project Overview
2. Features
3. Architecture
4. Getting Started
5. Usage
6. MLFlow Integration
7. Key Tools
8. Results
9. Contributing
10. License

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Project Overview 📖

Objective:

The primary goal of this project is to:

1. Collect live weather data 🌐.
2. Preprocess and clean the data 🧹.
3. Train machine learning models to predict weather conditions 🤖.
4. Automate workflows for scalability and reproducibility ⚙️.
5. Track models and experiments for easy version control 📊.

This project adopts MLOps best practices to ensure a seamless integration of machine learning into production workflows.

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Features ✨

• Automated Data Collection: Fetch real-time weather data using APIs.
• Data Versioning: Track datasets with DVC to ensure reproducibility.
• Workflow Automation: Manage and automate tasks with Airflow DAGs.
• Model Versioning and Experiment Tracking: Use MLFlow for logging experiments and comparing model performance.
• Scalability: Modular design for easy extension and deployment.

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Architecture 🏗️

Here’s an overview of the pipeline architecture:

graph TD;
    A[Fetch Weather Data 🌦️] --> B[Preprocess Data 🧹]
    B --> C[Train ML Model 🤖]
    C --> D[Evaluate and Monitor Results 📊]

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Each stage is modular and automated using Airflow, with DVC and MLFlow ensuring version control and tracking.

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Getting Started 🛠️

Prerequisites

• Python 3.8+
• Git
• DVC
• Apache Airflow
• MLFlow

Installation

1. Clone the repository:

   git clone https://github.com/your-repo/weather-mlops.git
   cd weather-mlops

2. Install dependencies:

   pip install -r requirements.txt

3. Initialize DVC:

   dvc init

4. Configure Airflow:

   airflow db init
   airflow users create --username admin --password admin --firstname Admin --lastname User --role Admin --email admin@example.com

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Usage 🏃‍♀️

1. Fetch Weather Data

Use the provided Python script to fetch live weather data:

python fetch_data.py --api_key YOUR_API_KEY --city "New York"

2. Run the Airflow Pipeline

Activate the Airflow scheduler and webserver:

airflow scheduler
airflow webserver

Access the Airflow UI at http://localhost:8080, enable the DAG, and watch the pipeline run! 🎡

3. Train and Track Models

Train models while logging parameters and metrics to MLFlow:

python train_model.py

Launch the MLFlow UI to view experiment results:

mlflow ui

Access it at http://localhost:5000.

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MLFlow Integration 📋

Overview:

MLFlow is integrated into the pipeline for:

• Logging model parameters (e.g., learning rate, batch size).
• Tracking metrics (e.g., accuracy, RMSE).
• Managing model versions and artifacts.
• Comparing experiment results.

Logging Model Experiments:

Here’s how the pipeline logs experiments to MLFlow:

1. Initialization: MLFlow is initialized with a remote tracking URI or a local directory.

   import mlflow
   mlflow.set_tracking_uri("http://localhost:5000")
   mlflow.set_experiment("Weather Forecasting")

2. Logging Parameters and Metrics: During training, parameters (e.g., hyperparameters) and metrics (e.g., validation accuracy) are logged:

   with mlflow.start_run():
       mlflow.log_param("learning_rate", 0.001)
       mlflow.log_metric("rmse", 2.3)

3. Storing Artifacts: Model artifacts (e.g., trained model files) are saved and versioned:

   mlflow.log_artifact("models/weather_model.pkl")

4. Model Registry: The best-performing model is promoted to production:

   mlflow.register_model("models:/WeatherModel/production", "weather_model")

MLFlow UI:

The MLFlow UI provides a comprehensive interface to:

• Compare experiments.
• Visualize metrics.
• Manage model versions.

To launch the UI:

mlflow ui

Access it at http://localhost:5000.

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Key Tools 🛠️

1. DVC: Tracks datasets and models, ensuring reproducibility.
2. Airflow: Automates pipeline tasks.
3. MLFlow: Logs and tracks experiments, parameters, and artifacts.
4. Weather API: Provides live weather data.
5. Scikit-learn: Used for model training.

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Results 📊

The pipeline outputs include:

• Cleaned Datasets: Version-controlled and stored using DVC.
• Trained Models: Versioned with DVC for reproducibility.
• Experiment Logs: Detailed metrics, parameters, and artifacts tracked using MLFlow.
• Automated Pipeline: Tasks run seamlessly using Airflow.

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Contributing 🤝

Contributions are welcome! Please follow these steps:

1. Fork the repository.
2. Create a new branch:

   git checkout -b feature-branch

3. Commit your changes:

   git commit -m "Add feature"

4. Push to the branch:

   git push origin feature-branch

5. Submit a pull request.

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License 📜

This project is licensed under the MIT License. Feel free to use and modify it as you like. 🎉

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Let’s Connect! 🌍

If you found this project helpful, feel free to:

• 🌟 Star the repo!

Happy coding! 🚀