The One with the Role of an MLOps Engineer

Explaining the roles, responsibilities and skillset of an MLOps engineer

Photo by Crystal Kwok on Unsplash

A majority of the Machine Learning (ML) models fail to cross the hurdle from a prototype to production.

Even though most industries target to build highly scalable and robust ML systems in production, it is often a challenge to automate and operationalise this procedure to ensure smooth execution.

This leads to the requirement of skilled professionals with not only a solid foundation in ML but also operations. To address this unique skillset, the role of an MLOps engineer was defined.

Who is an MLOps Engineer?

Source: Machine Learning Operations (MLOps): Overview, Definition, and Architecture (arxiv.org)

Developing an end-to-end ML product using the MLOps paradigm involves an interdisciplinary team comprising of Data Scientists, Data Engineers, Backend developers, DevOps engineers and Software Engineers.

An ML engineer or an MLOps engineer is often required to have cross-functional expertise in the intersection of Data Science, Data Engineering. DevOps, Backend development and, Software Engineering.

Besides, it also includes building and maintaining ML infrastructures, workflow orchestrations and monitoring of both model and infrastructure.

Skills needed to be an MLOps Engineer

As evident from above that an MLOps engineer needs to have interdisciplinary expertise in the area of Data Science, Data Engineering and Operations.

Technical Skills

Examples of some of the technical skills (not in any particular order) often searched in an MLOps engineer

Some of the top technical skills that are required by anyone transitioning to an MLOps engineer role are as follows:

• Programming expertise in Python and knowledge of one or more of the frameworks such as Tensorflow, Pytorch etc.
• Knowledge of web frameworks such as FastAPI, Flask etc.
• Experience with software development, version controlling
• A solid foundation in DevOps technologies like Docker, Kubernetes etc.
• Knowledge of Cloud infrastructures such as AWS, Microsoft Azure, Google Cloud Platform (GCP) etc.
• Popular MLOps frameworks (e.g. Kubeflow, MLflow, Airflow etc.) know-how

Non-technical or Soft Skills

Soft skills for an MLOps engineer

The role of an MLOps engineer is incomplete without the soft-skills. Some of the key non-technical skills required to have to become an MLOps engineer are the following:

• Good communication skills — this is an indispensable skill for any developer as they need to be able to communicate successfully with different people such as Data Scientists and Business owners.
• Critical thinking and problem-solving abilities — Machine Learning projects require any person involved to have excellent critical thinking and problem-solving abilities as it is often about fixing issues in production in real time.
• Team player — An MLOps engineer needs to be a team player as it is a role requiring interaction with different people with diverse backgrounds.

Get Started as an MLOps Engineer

Intimidated by the long list of technical know-hows to become an MLOps Engineer?

Thought so.

You can always start your developer journey by mastering your knowledge and experience in a part of the list, e.g. Data Science, Software development, or Cloud infrastructures and later transition into an MLOps professional.

Irrespective of your educational qualifications, contributing to the community through open-source projects can also be an option for not only beginners, but also intermediates, and experts in the field.

So what are you waiting for? Start your MLOps journey today!

✨ The references and further readings on this topic have been summarised here.

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