How to Upgrade from DevOps to Highly Paid MLOps Engineer

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 Roles and Responsibilities

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:

1. Continuous Integration/Continuous Deployment (CI/CD) Engineer:
   • Roles:
     • Design, implement, and maintain CI/CD pipelines for automated building, testing, and deployment of applications.
     • Ensure the reliability and efficiency of the deployment process.
   • Tools:
     • Jenkins, GitLab CI/CD, Travis CI, CircleCI, Azure DevOps, GitHub Actions.
2. Infrastructure as Code (IaC) Engineer:
   • Roles:
     • Define, manage, and automate infrastructure using code. for example suppose you need to setup kubeflow and mlflow for model registry then you might use terraform scripts to do that.
     • Implement configuration management for servers and infrastructure components.
   • Tools:
     • Terraform, Ansible, Chef, Puppet.
3. Monitoring and Logging:
   • Roles:
     • Implement monitoring solutions to track the performance and health of applications and infrastructure.
     • Set up logging systems for analysis and troubleshooting.
   • Tools:
     • Prometheus, Grafana, ELK Stack (Elasticsearch, Logstash, Kibana), Splunk, Nagios.
4. Security:
   • Roles:
     • Collaborate with security teams to implement and maintain security practices.
     • Implement security measures for infrastructure, applications, and data.
   • Tools:
     • Roles management ex IAM, OWASP tools, Qualys, Nessus, SonarQube.
5. Cloud Services:
   • Roles:
     • Architect, deploy, and manage applications and infrastructure in cloud environments.
     • Optimize cloud resources and costs.
   • Tools:
     • AWS, Azure, Google Cloud Platform (GCP), Kubernetes.
6. Version Control Engineer:
   • Roles:
     • Manage version control systems for code repositories.
     • Facilitate collaboration among development teams through proper branching and merging.
   • Tools:
     • Git, GitHub, GitLab, Bitbucket.
7. Containerization Engineer:
   • Roles:
     • Implement containerization solutions for packaging and deploying applications.
     • Manage container orchestration platforms.
   • Tools:
     • Docker, Kubernetes, OpenShift.
8. Automation:
   • Roles:
     • Automate repetitive tasks and processes to improve efficiency.
     • Develop scripts and automation workflows for various tasks.
   • Tools:
     • Shell scripting (Bash), Python, PowerShell.
9. Release Management:
   • Roles:
     • Coordinate and manage software releases across different environments and stakeholders.
     • Plan and execute deployment strategies, including rollback procedures.
   • Tools:
     • Release management tools (e.g., Jira Software, Octopus Deploy).

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 Engineer Roles and Responsibilities

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.

Understand Machine Learning Lifecycle conceptually:

• Understand the machine learning lifecycle from pipeline orchestration point of view. It is not like that as an MLOps engineer you need to train the model, that is the job of a data scientist, all coding related part needed at each step of machine learning lifecycle will be done by either data scientists or data engineers, mlops main responsibility is to integrate every piece of code in a workflow or pipeline and setup a CI/CD pipeline for efficient and reliable model deployment in production. Below is the Architecture diagram for ML lifecycle steps:

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:

1. Continuous Integration and Continuous Deployment (CI/CD) for ML:
   • Roles:
     • Set up and maintain CI/CD pipelines specific to machine learning workflows.
     • Automate the testing and deployment of machine learning models.
   • Tools:
     • Jenkins, GitLab CI/CD, TensorFlow Extended (TFX), MLflow.
2. Model Deployment and Serving:
   • Roles:
     • Deploy machine learning models into production environments.
     • Manage the serving infrastructure to handle predictions from deployed models.
   • Tools:
     • TensorFlow Serving, Flask, FastAPI, ONNX Runtime.
3. Model Monitoring and Logging:
   • Roles:
     • Implement monitoring solutions for tracking model performance and data drift.
     • Set up logging systems for capturing relevant information about model predictions.
   • Tools:
     • Prometheus, Grafana, MLflow, TensorFlow Data Validation.
4. Data Versioning and Lineage:
   • Roles:
     • Manage the versioning of datasets used in model training.
     • Establish data lineage to track the origin and transformations of datasets.
   • Tools:
     • DVC (Data Version Control), MLflow, Apache Atlas.
5. Feature Engineering and Transformation:
   • Roles:
     • Implement and manage feature engineering pipelines.
     • Ensure consistency between training and inference feature transformations.
   • Tools:
     • Featuretools, Pandas, scikit-learn.
6. Containerization and Orchestration:
   • Roles:
     • Containerize machine learning applications and models for portability.
     • Manage orchestration tools for deploying and scaling containers.
   • Tools:
     • Docker, Kubernetes, Apache Airflow.
7. Model Governance and Compliance:
   • Roles:
     • Implement policies and procedures to ensure model governance and compliance.
     • Monitor and enforce regulatory requirements for machine learning models.
   • Tools:
     • MLflow, ModelDB, Seldon Core.
8. Automated Testing for ML:
   • Roles:
     • Develop and implement automated testing frameworks for machine learning models.
     • Ensure robustness and accuracy of models through testing.
   • Tools:
     • PyTest, TensorFlow Data Validation, Great Expectations.
9. Collaboration and Communication:
   • Roles:
     • Facilitate communication and collaboration between data scientists, developers, and operations teams.
     • Share insights and updates on model performance and improvements.
   • Tools:
     • Slack, Microsoft Teams, Confluence, Jira.
10. Resource Optimization and Auto-scaling:
    • Roles:
      • Optimize resource usage for deployed machine learning models.
      • Implement auto-scaling strategies based on demand.
    • Tools:
      • Kubernetes, TensorFlow Serving with Kubernetes, Apache OpenWhisk.
11. Security for ML:
    • Roles:
      • Implement security measures for machine learning models and associated data.
      • Collaborate with security teams to address potential vulnerabilities.
    • Tools:
      • Open Policy Agent (OPA), HashiCorp Vault.
12. Cost Management:
    • Roles:
      • Optimize costs associated with machine learning infrastructure and services.
      • Monitor and manage resource consumption to avoid unnecessary expenses.
    • Tools:
      • Cost tracking tools provided by cloud providers, Kubernetes Cost Management tools.

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.

Summary

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.