Name: How to Upgrade from DevOps to Highly Paid MLOps Engineer
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How to Upgrade from DevOps to Highly Paid MLOps Engineer

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If you are a devops engineer and working in this role for more than three years then you can easily switch to highly paid mlops engineer and double your salary. As you are already aware of devops practices, tools and techniques to achieve software deployment in production efficiently and reliably. So with little effort and learning few extra techniques which I will be talking in next paragraphs, you can simply become the mlops engineer where you start deploying machine learning models in production. You might already be doing the same thing still your role says you are a devops and you are paid less. So this is the time to wake up and demand the right salary.
So, before knowing what is expected from an mlops engineer, it is very important to revisit what you as a devops engineer already know and what duties you perform on a day to day basis. Then I will explain what is left to become the mlops engineer and how much effort will be required. then you will be amazed to find that you already know about 80% and only 20% is left to learn and easily can upgrade yourself to an mlops engineer and get more than double the salary you are getting now.
DevOps engineers fulfill various roles within organizations, and their responsibilities often span multiple areas of the software development and delivery lifecycle. Here are some important job roles of a DevOps engineer, along with the tools commonly associated with each role:
Continuous Integration/Continuous Deployment (CI/CD) Engineer:
Infrastructure as Code (IaC) Engineer:
Monitoring and Logging:
Security:
Cloud Services:
Version Control Engineer:
Containerization Engineer:
Automation:
Release Management:
It’s important to note that the roles and tools mentioned may vary based on the specific needs and technologies adopted by different organizations. DevOps is a dynamic field, and DevOps engineers often need to adapt to new tools and practices as the technology landscape evolves, however, concepts remain same.
Now let us understand what is required by an mlops engineer, then you will realized that most of the things you as devops engineer already know.
MLOps (Machine Learning Operations) engineers play a crucial role in deploying, managing, and optimizing machine learning models in production. Their responsibilities involve collaboration with data scientists, software developers, and IT operations to ensure the seamless integration of machine learning models into operational systems. 
Most important thing I am mentioning in below two paragraphs and by following these things you can easily become mlops engineer. in later paragraphs I am mentioning in details the roles and responsibilities of mlops engineer so that you can easily map them with devops skills and gain confident that you already know many or all of them.
Now you read below roles and responsibilities of MLOps engineer (keeping in mind the devops roles which are already expert) and map software code with ml model which is nothing but just a binary file stored in some repository it could be github or more efficient some model registry example mlflow. Then you will realize that everything you already know, just finish understanding machine learning fundamentals and devlop understanding of each steps of ml lifecycle and practice one complete deployment of ml model using your devops skill and then you are already a mlops engineer.
Here are the roles and responsibilities of MLOps engineers along with some tools commonly associated with their tasks:
Continuous Integration and Continuous Deployment (CI/CD) for ML:
Model Deployment and Serving:
Model Monitoring and Logging:
Data Versioning and Lineage:
Feature Engineering and Transformation:
Containerization and Orchestration:
Model Governance and Compliance:
Automated Testing for ML:
Collaboration and Communication:
Resource Optimization and Auto-scaling:
Security for ML:
Cost Management:
It’s important to note that the MLOps landscape is evolving, and new tools and practices are continually emerging. The specific tools used by MLOps engineers can vary based on the organization’s preferences, infrastructure, and requirements. The roles and responsibilities outlined above reflect the key aspects of the MLOps engineer’s role in deploying and maintaining machine learning models in production environments.
So by now you already know the tools and techniques used by an mlops engineer. And I would say these are mostly same as of devops engineer. Only the thing is everything you need to map around an machine learning model and artifacts like data versioning tools like dvc, feature store tools like feast, model versioning tools like mlflow, code versioning tools like git, pipeline tools like Airflow and Kubeflow, CI/CD tools like Gitlab CI/CD, Github Actions, specific libraries just discuss with data scientists and create docker images for those. Use terraform to develop Infrastructure as a code which you already do.
By combining your existing DevOps skills with a solid foundation in machine learning and MLOps practices, you can position yourself for a successful transition to a highly paid MLOps engineer role.

If you are a devops engineer and working in this role for more than three years then you can easily switch to highly paid mlops engineer and double your salary. As you are already aware of devops practices, tools and techniques to achieve software deployment in production efficiently and reliably. So with little effort and learning few extra techniques which I will be talking in next paragraphs, you can simply become the mlops engineer where you start deploying machine learning models in production. You might already be doing the same thing still your role says you are a devops and you are...

How to Upgrade from DevOps to Highly Paid MLOps Engineer

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