Roles in MLOps

MLOps (Machine Learning Operations) is a practice that combines the principles of DevOps with the unique requirements of machine learning to improve the speed, quality, and reliability of machine learning models in production. In this practice, several roles are defined to manage the end-to-end machine learning pipeline:

1. Data Engineer Data Engineer is responsible for data collection, storage, and processing. They ensure that data is properly labeled, annotated, and cleaned before it is used for training models.

2. Machine Learning Engineer Machine Learning Engineer is responsible for designing, developing, and deploying machine learning models. They work closely with data engineers and data scientists to ensure that models are properly trained and optimized.

3. Data Scientist Data Scientist is responsible for exploring and analyzing data, developing models and algorithms, and interpreting the results. They work closely with machine learning engineers to ensure that models are properly trained and validated.

4. DevOps Engineer DevOps Engineer is responsible for automating and streamlining the deployment, scaling, and management of machine learning models in production. They work closely with machine learning engineers and data engineers to ensure that models are properly deployed, monitored, and maintained.

5. Model Governance Model Governance person is responsible for managing and controlling the lifecycle of machine learning models. They work closely with data scientists, machine learning engineers, and devops engineers to ensure that models are properly versioned, tracked, and audited.

6. MLOps Engineer MLOps Enginner is responsible for coordinating and managing the end-to-end machine learning pipeline. They work closely with data engineers, machine learning engineers, data scientists, devops engineers, and model governance to ensure that models are properly integrated, deployed, and maintained in a production environment.

Specialisations in MLOps Engineering

The generic MLOps Engineer role can be broken down into several more specific roles:

1. Model Deployment Engineer

Model Deployment Engineer is responsible for deploying and managing machine learning models in production. They work closely with machine learning engineers and DevOps engineers to ensure that models are properly deployed, scaled, and maintained in a production environment.

2. Data Pipeline Engineer

Data pipeline Engineer is responsible for building and maintaining data pipeline that feeds the machine learning models. They work closely with data engineers to ensure that data is properly collected, stored, and processed before it is used for training models.

3. Model Monitoring Engineer

Model Monitoring Engineer responsible for monitoring the performance and health of machine learning models in production. They work closely with machine learning engineers, data engineers, and DevOps engineers to ensure that models are properly monitored, troubleshot, and maintained in a production environment.

4. Model Governance Engineer

Model Governance Engineer is responsible for managing and controlling the lifecycle of machine learning models. They work closely with data scientists, machine learning engineers, and DevOps engineers to ensure that models are properly versioned, tracked, and audited.

5. Machine Learning Infra Engineer

Machine Learning Infra Engineer is responsible for building and maintaining the infrastructure that supports machine learning models in production. They work closely with machine learning engineers, data engineers, and DevOps engineers to ensure that the infrastructure is properly configured, scaled, and maintained in a production environment.

6. Machine Learning Platform Engineer

Machine Learning Platform Engineer is responsible for building and maintaining the platform that supports machine learning models in production. They work closely with machine learning engineers, data engineers, and DevOps engineers to ensure that the platform is properly configured, scaled, and maintained in a production environment.

Some of the roles and responsibilities may overlap depending on the organization and the specific requirements of the project.

Credits:

Header image from Unsplash by Natalie Pedigo

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@article{Saf2022Roles,
    author  = {Krystian Safjan},
    title   = {Roles in MLOps},
    journal = {Krystian's Safjan Blog},
    year    = {2022},
}