Who are the people in MLOps?
Data Science and MLOps are constantly evolving and have multiple roles and people.
Not only Data Scientists but many other people are also there in the ML Model lifecycle.
But who are they and what do they do?
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1. Subject Matter Experts
Data scientists may not have a deep understanding of the businesses and domains they work for.
SMEs help them frame the problem better by working alongside data scientists.
They have defined goals, business questions and KPIs.
2. Data Scientists
They do the analysis, experimentations and build the ML models.
They are not always included in deploying or testing the model but the roles can overlap.
They handle the constant assessment of the model quality after deployment.
3. Software Engineers
It is important to have good software engineers working with data scientists so that larger systems can be built efficiently.
It is also important that the ML code and models deployment fit into the CI/CD pipeline of the existing software as well.
4. DevOps
DevOps have two primary roles in the ML models life cycle:
First, they conduct and build operational systems and create tests to ensure security, performance and availability of ML models.
Second, creating CI/CD pipelines is also generally their responsibility.
5. Model Risk Manager
MRM is important for regulatory compliance for larger industries.
They analyze the model outcomes and business goals that ML is supposed to solve.
They have to ensure that model poses no risk to the business in any way.
6. Machine Learning Architect
They are responsible for overall architecture and ensuring requirements of data needs.
ML architects ensure a flexible environment for pipelines.
They provide expertise in new technologies that may improve model performance in production.
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