rohitjakkam / mlops-roadmap

Roadmap for MLOps

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Machine Learning Operations(MLOps) Roadmap 🤖

This repository is a comprehensive guide to anyone who wishes to learn MLOps. It contains a roadmap with various topics and resources. You can find detailed videos on these topics on my YouTube channel (linked in the description). For a more in-depth understanding, consider enrolling in my comprehensive course.

Table of Contents

I will divide the resources into different levels of learning and will also provide the best resources to learn each topic. The levels of learning are:

• **Machine Learning and Data Science Basics
• **Software Development Life Cycle (SDLC)
• **DevOps
• **MLOps
• **My Courses

Machine Learning and Data Science Basics

Before you start your journey, it's essential to have a solid base in machine learning and data science. Please refer to my Machine Learning Roadmap Video for a structured approach to these subjects.

Software Development Life Cycle (SDLC)

Understanding the Software Development Life Cycle is the next step:

• Requirement Gathering Analysis: Understand what the client needs and determine the feasibility of the requirements.
• Design: Plan and design the software-based on the requirement analysis.
• Implementation or Coding: Build the software by integrating the pieces of code and libraries.
• Testing: Test the software for any bugs and ensure it works as expected.
• Deployment: Release the software on live servers where actual users will use it.
• Maintenance: After deployment, introduce updates and improvements to the software.

You can find the detailed resources related to SDLC in this GitHub Repository.

DevOps

Next, learn about DevOps. The important topics include:

• Continuous Integration / Continuous Delivery (CI/CD)
• Infrastructure as Code (IAC)
• Version Control Systems (like Git)

MLOps

Once you have a good grasp of DevOps, it's time to dive into MLOps:

• Data Versioning: Tools like DVC (Data Version Control)
• Model Versioning: Tools like MLflow and DVC
• Model Packaging: Tools like Docker
• Model Validation and Testing: Tools like TensorFlow Extended (TFX) and PyCaret
• Continuous Integration for Machine Learning: Tools like Jenkins and GitHub Actions
• Continuous Deployment for Machine Learning: Tools like Jenkins, GitHub Actions, and Azure DevOps
• Model Monitoring and Retraining: Tools like ModelDB, MLflow, and TFX
• Governance and Regulatory Compliance: Tools like IBM OpenPages and Collibra

My Courses

• Core Machine Learning Course: For a comprehensive, paid course on Machine Learning, please visit this link.
• Free 10-hour Video Course: For a free, 10-hour long introduction to Machine Learning, please watch this video.

Contributions 🤝

We are open to contributions, if you want to contribute to this repository, you can check out the contributing guidelines. You can also contribute by sharing this repository with your friends and colleagues.