A Beginner-to-Upper Intermediate Data Science Roadmap for 2025 #12: Machine Learning Operations (MLOps) Fundamentals

A Step-by-Step Roadmap to Start a Data Science Career In 2025

There are many challenges in bringing your machine learning systems into production, which include construction, integrating, testing, releasing, deployment, and infrastructure management.

Therefore it is important to follow good practices and know how to overcome these challenges. MLOps technologies are tools and platforms that help organizations manage and optimize machine learning models’ development, deployment, and maintenance.

In the 12th part of this series, we will provide you with a comprehensive guide to learning about MLOps, including the key concepts and skills that you need to master. We will also provide you with a selection of the best free learning resources available online to help you get started on your MLOps journey.

Whether you are new to MLOps or have some experience under your belt, this roadmap will provide you with a clear and structured path to follow to help you become an expert in this exciting and rapidly evolving field. So, let’s get started!

This article is the Twelveth article in the ongoing series of A Beginner-to-Upper Intermediate Data Science Roadmap for 2025:

• Introduction to Data Science & Data Methodology (Published!)

• Mathematics for Data Science (Published!)

• Python Fundamentals (Published!)

• Python for Data Science (Published!)

• Software Engineering Basics (Published!)

• Database & SQL Fundamentals (Published!)

• Data Cleaning & Preprocessing (Published!)

• Feature Engineering (Published!)

• Mastering Machine Learning (Published!)

• Deep Learning Fundamentals (Published!)

• Generative AI & Large Language Models (LLMs) Fundamentals (Published!)

• Machine Learning Operations (MLOps) (You are here!)

• Building Your Data Science Portfolio (Coming Soon!)

• Getting Ready for the Market (Coming Soon!)

Whether you’re a recent graduate or a professional looking to make a career change, Data Science and AI offer a wide range of exciting and lucrative opportunities.

In this series of articles, I will provide a comprehensive guide with a clear and actionable plan for building the skills and knowledge you need to succeed in this growing field. By following the steps outlined in this roadmap, you’ll be well on your way to a successful and rewarding Data Science and AI career.

This roadmap will take you to an upper intermediate level, and you can land a job and start your career after finishing it. However, to go to an advanced level, you must take more in-depth courses, books, and research papers.

For each learning step, there will be compulsory material, optional material, and action points to ensure that you implement what you have learned Also, each of the learning resources will be estimated in hours, so you can calculate the time needed to finish this roadmap based on your pace.

Table of Contents:

1. Version Control for Machine Learning

2. Continuous Integration & Continuous Delivery (CI/CD) Tools

3. Infrastructure & Resource Management for Machine Learning

4. Machine Learning Monitoring & Observability Tools

5. Managing Machine Learning Projects & Pipelines

6. Machine Learning Security & Compliance Tools

7. Putting it into Action

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