/Mlflow

This repository provides a foundational guide to MLOps, including tools and workflows for model versioning, data versioning, CI/CD pipelines, and experiment tracking. It features examples and use cases in Python, Jupyter Notebook, and Google Colab, along with integration with DagsHub for collaborative machine learning.

Primary LanguagePython

MLOps Basic Repository

This repository provides a comprehensive guide for implementing basic Machine Learning Operations (MLOps) principles. It includes various tools and methods for model deployment, version control, and CI/CD pipelines, with examples and use cases in Jupyter Notebook, Google Colab, and Python. We also utilize DagsHub for tracking experiments, datasets, and collaboration in ML projects.

Table of Contents

Applications

This repository covers the following MLOps applications:

  1. Model Versioning: Keeping track of different versions of machine learning models.
  2. Data Versioning: Handling datasets efficiently across versions.
  3. CI/CD Pipelines for ML: Continuous integration and delivery for machine learning models.
  4. Experiment Tracking: Tracking hyperparameters, results, and metrics from experiments.
  5. Model Deployment: Deploying models to various platforms (cloud, local).
  6. Monitoring Models in Production: Ensuring models are performing as expected post-deployment.
  7. DagsHub Integration: Versioning datasets, models, and collaboration using DagsHub.

Installation

Prerequisites

  • Python 3.7+
  • Git

Steps to Install

  1. Clone this repository:

    git clone https://github.com/your-username/mlops-basic.git
cd mlops-basic

  2. Create a virtual environment and activate it:

    python -m venv venv
source venv/bin/activate   # For Linux/MacOS
venv\Scripts\activate      # For Windows

  3. Install dependencies:

    pip install -r requirements.txt

  4. Install DagsHub CLI:

    pip install dagshub

Running on Different Platforms

Running on Jupyter Notebook

To run the MLOps workflows on Jupyter Notebook:

  1. Launch Jupyter Notebook: 
    jupyter notebook
  2. Navigate to the notebook folder and open any .ipynb file. Follow the instructions in the notebook to run MLOps pipelines.

Running on Google Colab

You can also run the notebooks in Google Colab:

  1. Open Google Colab and upload the .ipynb files from the notebooks/ directory.
  2. Ensure the required libraries are installed by running: 
    !pip install -r requirements.txt

Running with Python

For running scripts directly with Python, use:

python scripts/train_model.py

or other relevant scripts inside the scripts/ folder. Modify the configurations inside the script or pass arguments through the command line.

Running with DagsHub

DagsHub is integrated to track models, datasets, and experiments:

  1. Initialize DagsHub inside the project: 
    dagshub init
  2. Push model and dataset versions: 
    dagshub push --model models/model_v1.pkl --dataset datasets/data_v1.csv
  3. View experiment results and collaborate via DagsHub’s web UI.

Functionalities and Use Cases

1. Model Versioning

  • Track and manage different versions of ML models.
  • Use case: Comparing performance between models with different architectures.

2. Data Versioning

  • Keep track of the datasets used across different stages of the ML pipeline.
  • Use case: Handling changes in data distributions or feature engineering changes.

3. CI/CD Pipelines

  • Automate training, validation, and deployment using CI/CD pipelines.
  • Use case: Ensuring reproducibility of experiments across environments.

4. Experiment Tracking

  • Automatically track experiments using libraries like mlflow or wandb.
  • Use case: Tracking hyperparameters, metrics, and model artifacts.

5. Model Deployment

  • Deploy models to platforms like AWS, GCP, or serve them via Flask/Django.
  • Use case: Scaling model inference in production.

6. Monitoring Models

  • Integrate monitoring tools to check the performance of models in production.
  • Use case: Detecting model drift or data distribution changes.

7. DagsHub Integration

  • Use DagsHub for versioning datasets, models, and experiment tracking.
  • Use case: Collaborative machine learning workflows across teams.

DagsHub Integration

DagsHub is integrated into this repository for:

  • Dataset versioning.
  • Model versioning.
  • Tracking experiments and hyperparameters.
  • Collaborative work with visualized progress.

To start using DagsHub, create an account at dagshub.com and follow the steps in the installation section to push models and datasets.

Pushing Data and Models to DagsHub

To push data and models to DagsHub, first link your repository by running:

dagshub connect <repository_url>

Then, push your model and datasets with the following command:

dagshub push --model <model_path> --dataset <dataset_path>

Contributing

Contributions are welcome! Feel free to submit a pull request or raise an issue. Please follow the contribution guidelines to ensure your PR is accepted.

License

This repository is licensed under the MIT License - see the LICENSE file for details.

By including detailed sections about installation, running on different platforms, functionalities, and DagsHub integration, this README.md will serve as a clear guide for users of your MLOps repository.