MLOps Project

MLOps Project#

MLOps Team Project: End-to-End Machine Learning Pipeline from Bare Metal

Objective#

This project aims to teach students the process of building an end-to-end machine learning pipeline starting from a bare metal system. The project will cover setting up the operating system, configuring a machine learning framework, and training a machine learning model. The final product will be shared with the class via a GitHub repository.

Requirements#

  • Basic knowledge of Linux operating systems

  • Familiarity with machine learning concepts and frameworks

  • Git and GitHub for version control and collaboration

  • Experience with Docker and Kubernetes for containerization and orchestration (optional but recommended)

Project Overview#

1. Project planning and team organization#

  • Define project scope and objectives

  • Divide the class into sub-teams: OS setup, ML framework setup, model development, and MLOps setup

  • Set up a project management tool (e.g., Trello) to track progress and assign tasks

2. Operating system setup#

  • Choose a Linux distribution (e.g., Ubuntu, CentOS)

  • Install and configure the opera.ing system on a bare metal server

  • Set up user accounts and permissions

  • Install and configure essential tools (e.g., SSH, Git)

3. Machine learning framework setup#

  • Choose an ML framework (e.g., TensorFlow, PyTorch)

  • Install and configure the ML framework on the server

  • Set up dependencies and libraries

  • Test the framework with a sample ML model

4. Model development#

  • Select a problem and dataset (e.g., image classification, sentiment analysis)

  • Perform data preprocessing and feature engineering

  • Train and evaluate the model

  • Fine-tune the model using hyperparameter optimization

5. MLOps setup#

  • Containerize the ML model using Docker

  • Set up a CI/CD pipeline using tools like Jenkins or GitHub Actions

  • Deploy the model on a Kubernetes cluster (optional)

  • Monitor the model performance and log relevant metrics

  • Implement automated testing and validation

6. Finalization and documentation#

  • Consolidate the work of each sub-team

  • Create comprehensive documentation on the project, including setup guides, model development, and deployment

  • Share the final project on GitHub, ensuring that all team members have contributed to the repository

  • Prepare a project presentation for the class, highlighting the main steps, challenges, and outcomes

Outcomes#

  • Students will gain hands-on experience in setting up an end-to-end machine learning pipeline.

  • Students will learn how to collaborate and divide tasks among team members effectively.

  • Students will understand the importance of version control and documentation in MLOps.

  • The final GitHub repository will serve as a resource for the class and future students.

Contents