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TL;DR

MLOps Framework is a lightweight, declarative data pipeline engine designed to simplify the orchestration of ML and data workflows. Built around PySpark, Delta Lake, MLflow, and Pydantic, it focuses on creating reproducible, scalable, and metadata-driven pipelines for modern machine learning applications.

Introduction

This project was built to address a common challenge in ML development: turning experimental data workflows into something more structured, reusable, and production-friendly. The framework emphasizes clarity and automation, helping teams define pipelines declaratively while preserving important metadata, validation, and tracking across the lifecycle of data and model work.

🚀 Key Features

✅ Declarative pipeline definitions for clean orchestration
🧠 Integration with PySpark for distributed data processing
📦 Delta Lake support for reliable data lake workflows
📊 MLflow integration for experiment tracking and model management
🛡️ Pydantic-driven validation for configuration integrity
🔁 Reproducible, metadata-aware execution patterns

🛠️ Tech Stack

• Language: Python
• Data Processing: PySpark
• Storage: Delta Lake
• ML Lifecycle: MLflow
• Validation: Pydantic
• Notebook & Experimentation: Jupyter Notebook

📸 Screenshots

📚 How to Use

To get started with the project:

1. Clone the repository
   bash

   git clone https://github.com/Tr1ck-5t3r/mlops_framework.git
   cd mlops_framework

2. Create and activate a Python environment:
   bash

   python -m venv .venv
   source .venv/bin/activate

3. Install the required dependencies:
   bash

   pip install -r requirements.txt

4. Define or load a pipeline configuration and run the workflow using the framework’s pipeline entry points.

Conclusion

MLOps Framework reflects a strong focus on building reliable machine learning infrastructure rather than isolated experiments. It brings together modern data engineering and MLOps principles in a lightweight package, making it easier to manage complex data pipelines and production-ready ML workflows with repeatability and structure.