Data science and MLOps explained: Roles of MLOps engineers and data scientists – similarities and differences

Machine Learning is one of the most exciting subfields of artificial intelligence but it is so broad that there is a need for specialisation. One such specialisation taking the AI industry by storm is called MLOps. Machine Learning Operations, simply called MLOps, is a field that lies at the intersection of DevOps, data engineering, and machine learning.

Data science, on the other hand, is an interdisciplinary field of scientific methods, processes, algorithms, and systems. Data scientist, one of the key roles in data science, uses the learning to extract knowledge and insights from structured and unstructured data. The field also allows professionals to apply their knowledge and actionable insights from data across a broad range of application domains.

In simple terms, MLOps is essentially about the operation of an algorithm and not the research involved in building or designing that algorithm. A professional working on the operation of this algorithm is usually called an MLOps engineer.

Data science is also about algorithms only but involves both operations, technical, and business implementation. In order to understand the difference between data science and MLOps, we need to differentiate them through the lens of key roles in these two spaces.

Machine Learning Ops: What is it and what does an MLOps engineer do

As mentioned earlier, Machine Learning Ops is a specialisation field that looks after the operational aspect of an algorithm. MLOps is generally seen as part of the data science team and not as a separate entity. The people working in the field of MLOps are usually called as MLOps engineers and it is often a software engineer who would transition to this role.

Even a data scientist can transition to an MLOps engineer role if one is interested in software engineering, data engineering, and the deployment of models. The role of an MLOps engineer is primarily to find ways to optimise some of the data science code. It then requires connecting the gap between testing and production within a software system.

An MLOps engineer will usually be found studying the general concepts of the machine learning algorithm and understanding how often the model needs to be trained, tested, and deployed. The professionals working in MLOps are also expected to use their expertise to automate the whole workflow, and work on code repository creation or efficiency.

In a nutshell, the field of data science and MLOps may seem distinct but they overlap a lot in their nature. As a result, you will often find an MLOps engineer working closely with a data scientist to understand the business problem and find a solution using the data available from the system.

Data Science: what is it and what does a data scientist do

As an interdisciplinary field, data science encompasses everything that has to do with scientific methods, algorithms, processes, and systems necessary to extract knowledge and insights. This is usually derived from a set of structured or unstructured data.

Data science has become so broad in the past few years that the definition varies depending on when you began learning the field. The best way to understand data science is to be in the shoes of a data scientist.

A data scientist is a business focussed scientist working to study, find, and solve problems seen within the company. This is usually done with the help of machine learning algorithms and as mentioned earlier, data scientists will work with an MLOps engineer to streamline the algorithm but not necessarily look at the operational aspect of the algorithm.

If an MLOps engineer has refined the algorithm to such an extent that it has reached its efficiency, a data scientist will then look to make the overall process more efficient or accurate to derive the best results for the business. It is thus no surprise that these two roles coexist in an AI-fueled organisation.

A data scientist will generally begin their work by examining the data and products from their company. They will then discuss the pain points of the business and come up with solutions by working with a data engineer. They will also then select the right model to use for the business functionality, remove redundancy and present the company with an efficiency model.

Similarities between data science and MLOps

Data science is a broad subject while MLOps is a specialisation subject but they share a lot between them in terms of deployment and functioning. Here is a look at some of the similarities.

  • Both data science and MLOps require an understanding of the business at the highest level. This includes understanding the problem and coming up with a solution.
  • People in the field of data science and MLOps are required to be proficient in Python and SQL
  • Both the fields require their practitioners to be well versed in the concept of training and testing
  • A data scientist and an MLOps engineer are expected to work with Git and Github
  • The fundamental requirement in data science and MLOps is for a person to know everything about their company data, and be prepared to find more data by knowing where to look for it

Differences between data science and MLOps

The difference between data science and MLOps comes down to how an algorithm is created or deployed. The best way to see the difference is through the lens of operation and business application. Here is a look at some of the differences between these two growth fields.

  • Data science is a research-oriented field and thus does not deal with the operational aspect
  • MLOps is all about working on production-ready code and being proficient in coding
  • A data scientist will have a master’s degree in data science
  • An MLOps engineer will usually have a software engineering degree and usually possess an undergraduate degree or bachelor’s degree
  • A data scientist is required to know how the actual machine learning algorithm works and focus on creating and choosing the right algorithm
  • MLOps role requires knowledge about DevOps tools like Docker as well as expertise in cloud services like Amazon Web Services (AWS), Google Cloud, and others.

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