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Snowflake MLOPS Project.

Introduction

Welcome to the Snowflake MLOPS Project repository. This project demonstrates a complete MLOPS workflow using Snowflake, which includes data loading, preprocessing, model training, and inference. The goal is to fully leverage Snowflake's capabilities to streamline machine learning pipelines in the cloud.

Project Structure

  • .github/workflows/: Contains GitHub Actions for CI/CD.
  • Notebooks/: Jupyter notebooks that detail each step of the ML process.
    • 1_load_data.ipynb: Data ingestion into Snowflake.
    • feature_engineering.ipynb: Data cleaning and transformation.
    • train_save_model.ipynb: Model training and serialization.
  • dataset/: Sample dataset used for training.
    • advertising.csv: Advertising data with metrics like TV, radio, and newspaper spend.
  • Dockerfile: Defines the Docker environment for the project.
  • docker-compose.yml: Manages the Docker services.
  • requirements.txt: Lists all dependencies required for the project.

Features

  • Data Loading: Automated scripts to load data directly into Snowflake using Snowpark.
  • Feature Engineering: Transformation scripts to prepare data for modeling.
  • Model Training: Utilization of Snowflake's in-database capabilities to train models.
  • Inference: Deployment of a user-defined function (UDF) in Snowflake for real-time predictions.

Getting Started

Prerequisites

  • Docker installed on your machine.
  • Access to a Snowflake account.

Installation

  1. Clone this repository:
    git clone https://github.com/<your-username>/snowflake-mlops.git
    cd snowflake-mlops

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