Prefect-mlops

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This repository contains an implementation of MLOps workflows using Prefect, an open-source workflow automation platform. The aim of this project is to demonstrate best practices for managing machine learning workflows in a production environment, including data preprocessing, model training, deployment, and monitoring.

Overview

Prefect MLOps provides a streamlined framework for orchestrating end-to-end machine learning pipelines. It integrates seamlessly with popular machine learning libraries such as TensorFlow, PyTorch, and scikit-learn, allowing users to define and execute complex workflows with ease.

Features

• Workflow Automation: Define and automate machine learning pipelines using Python-based workflows.
• Flexible Integration: Seamlessly integrate with various data sources, machine learning frameworks, and deployment platforms.
• Version Control: Track changes to workflows and pipeline components using version control systems like Git.
• Monitoring and Alerting: Monitor pipeline performance and receive alerts for anomalies or failures.
• Scalability: Scale workflows horizontally and vertically to accommodate large datasets and computational resources.
• Containerization: Containerize workflows and models for portability and reproducibility across environments.

Getting Started

To get started with Prefect MLOps, follow these steps:

1. Clone the Repository: Clone this repository to your local machine using the following command:

   git clone https://github.com/seunboy1/Prefect-mlops.git

2. Install Dependencies: Install the required dependencies by running:

   pip install -r requirements.txt

3. Start the Prefect server locally: Create another window and activate your conda environment. Start the Prefect API server locally with

   prefect server start

Optional: use Prefect Cloud for added capabilties

Signup and use for free at https://app.prefect.cloud