MLOps: How to become a MLOps Engineer?

What is MLOps?

Few years back we use to talk and learn about software development lifecycle(SDLC) and the process it goes from requirement → designing → development → testing → deployment → and then maintenance. We were implementing the waterfall model moved to iterative model then to agile model of software development to DevOps.

But, now organisations are trying to integrate AI/ML into their process. This new concept of building ML systems adds/reforms some principles of the SDLC to give rise to a new discipline called MLOps.

MLOps is communication between data scientists and the operations or production team. It’s deeply collaborative in nature, designed to eliminate waste, automate as much as possible, and produce richer, more consistent insights with machine learning.

This concept is deeply collaborative in nature between data scientists and the operations or production team, designed to eradicate waste, automate as much as possible, and produce richer, more consistent insights with machine learning (ml).

Who should learn MLOps?

  • DevOps engineers
  • Data scientists
  • Data engineers
  • ML engineers
  • Data and Analytics Manager
  • Business Analysts
  • IT/Software Engineers
  • Model Risk Managers/Auditors

What are the prerequisites to learn MLOps?

Basics of DevOps & Machine learning will help.

What are the benefits of MLOps?

  • Reduced time to market of AI-driven products
  • Enhanced user experience due to the fact that apps get timely updates
  • Higher quality of predictions
  • The ability of data engineers to focus on building new models — instead of diving into deployment routine.
  • Rapid innovation through robust machine learning lifecycle management.
  • Creation of reproducible workflows and models.
  • Easy deployment of high precision models in any location.
  • Effective management of the entire machine learning life cycle.
  • Machine Learning Resource Management System and Control.

Why we should learn MLOps?

  • Machine learning techniques have finally achieved enough effectiveness and practicality for mass adoption in companies and institutions. MLOps practice is going to be next buzzword of the industry.
  • MLOps is still emerging – most companies are in the early stages of understanding and proficiency of this concept. The potential for MLOps professionals in this area, therefore, is enormous.
  • ML-based solutions and MLOps become are going to be a crucial for businesses, the trend is for the degree of specialization to increase.
  • In the recent four years, the hiring for MLOps roles has grown upto 74% annually.
  • MLOps is predicted to grow rapidly in the coming years and is estimated to reach up to $4.5 billion by the end of 2025.
  • MLOps being a new field, is witnessing a shortage of skilled professionals.

What is the future of MLOps?

During the few short years MLOPS has grown in popularity, several open source frameworks have emerged. In a move that shows the importance of this practice, as data and technology continue to expand and reach new heights, ML will help organizations of all types develop strong strategies, manage and succeed in the future.

How DevOpsSchool’s MLOps course/Training would help?

DevOpsSchool has a team of passionate instructors, educators, trainers, and mentors. We are highly motivated who build fresh and lasting learning experiences for our participants. Powered by our innovation processes, we provide an environment where learning is easy, constructive, and fruitful.


This MLOps course is a program which tackles the subject of deploying the Machine Learning models in production and at scale. Our MLOps course will help you to learn – best MLOps tools, techniques, and practices for deploying, evaluating, monitoring and operating production ML systems end-to-end. As part of this course, you will learn to deploy models into production environments using cutting edges open-source frameworks.