Difference Between Data Scientist & MLOps Engineer | Towards Data Science

IntroductionData ScientistMachine Learning Operations EngineerSimilarities and DifferencesSummaryReferences
Introduction
Data Scientist
Machine Learning Operations Engineer
Similarities and Differences
Summary
References
While I have written articles on Data Science and Machine Learning Engineering roles, I wanted to compare the specific positions of Data Scientists and Machine Learning Operations Engineers, often referred to as MLOps Engineers. Machine Learning itself can be incredibly broad, so as a result, a newer career has emerged that solely focuses on the operations rather than the research that goes behind the algorithms themselves. Data Scientists ironically focus more on Machine Learning algorithms than does an MLOps Engineer. You could even go as far as saying an MLOps Engineer is a Software Engineer traditionally who has then added the specialization of deployment and production parts of the overall Data Science process. I will be diving deeper into these two positions, so keep on reading if you would like to learn more about the common differences and similarities between these two prominent careers.
As more specializations emerge in the field of Data Science, the role of Data Scientist is becoming broader. Therefore, you can expect my experiences to include some differences from yours or your expectations. However, I have studied Data Science in a variety of forms, and want to give the best overview of the position, along with key points that distinguish it from an MLOps role. A Data Scientist can best be described as a business-focused scientist who studies, finds, and solves problems within the company with Machine Learning algorithms. Your main goal will usually be to make a process both more accurate and efficient compared to how it was previously done. This definition sounds quite similar to a Software Engineering role, however, this position focuses of course more on the algorithms and how they work as the solutions rather than more hand-made and object-oriented programming code solutions.
Here is a general process that a Data Scientist can expect at their company over the course of several days to several months:
explore the data and products of your companymeet with stakeholders who have previously identified pain points in the business either internally or externallycome up with a business problem statement that highlights the issue at handobtain the data in some way, usually with SQL or working with a Data Engineer to digest newer data from other, new sourcesperform exploratory data analysis on your chosen datasetcompare several models compared to a baseline modelchoose your main algorithmidentify key features — feature engineeringremove redundant and unnecessary featurespossibly create an ensemble or stepwise algorithm processaccount for outlierssave your model and test in a dev environmentprovide details on accuracy or error metricspresent how much you can save the company, and how you can make the product better
explore the data and products of your company
meet with stakeholders who have previously identified pain points in the business either internally or externally
come up with a business problem statement that highlights the issue at hand
obtain the data in some way, usually with SQL or working with a Data Engineer to digest newer data from other, new sources
perform exploratory data analysis on your chosen dataset
compare several models compared to a baseline model
choose your main algorithm
identify key features — feature engineering
remove redundant and unnecessary features
possibly create an ensemble or stepwise algorithm process
account for outliers
save your model and test in a dev environment
provide details on accuracy or error metrics
present how much you can save the company, and how you can make the product better
Sometimes these steps in the Data Science process that I have listed above can change from time to time. You want to keep in mind that all businesses are different but if you follow a process of data creation, algorithm comparison, testing, and presentation of results, you will be a great Data Scientist for your company.
Now, what happens after step 14? That is where the next role comes into play. However, also keep in mind that not every company can afford or deems it necessary to have an MLOps Engineer alongside a Data Scientist. But, if you are lucky enough to have a more full Data Science team, you can focus on those steps from above, and the MLOps Engineer can focus on the next steps that I will describe below. Of course, it would be great if you could do both, and a lot of people work in both roles. Sometimes though, it can be more beneficial if a Data Scientist focuses more on the Machine Learning algorithms and the MLOps Engineer focuses on the deployment, pipelines, and productionalization of your Data Science model. It can help you to improve on your model's accuracy or reduce its error metrics when you do not have to worry about the software engineering heavy aspects of implementing the actual model into your business, phone application, and other parts of your company’s software.
MLOps Engineer
The Machine Learning Operations Engineer role, often referred to as an MLOps Engineer is important and beneficial to have on your Data Science team. You may find yourself switching to this role if you are a current Sofware Engineer and want to work cross-functionally, yet specifically with Machine Learning algorithms, or you could be a current Data Scientist who has knowledge of how the algorithms work, but want to focus more on the Software Engineering, Data Engineering, and deployment of models. You may first be handed a Data Science model that has been developed by a Data Scientist when you are working as an MLOps Engineer. You will also work on seeing on how to optimize some of the Data Science code, since you will be more Sofware Engineering focused with more object-oriented programming experience (once again, this experience may be different for you or your company, but generally, I have seen MLOps being more proficient in programming).
So, in general, as an MLOps Engineer, you can expect to work with Data Scientists to connect the gap from testing to production within your company software with the practice of both Data Engineering and DevOps tools.
Here is a general process that an MLOps Engineer can expect at their company over the course of several days to several months:
study the general concepts of the Machine Learning algorithm(s) usedunderstand the business problem and the Data Science solutionunderstand how often the model would need to be trained, tested, and deployedhow many predictions will you make and when?but more importantly (the Data Scientists can work on steps 2–4 as well), see how you can use your expertise to automate the whole workflowalso, to implement the model within the app or software of your company (e.g., inserting model results hourly into a table that shows into a UI that a consumer sees)ultimately optimize the model itself with Data Engineering techniques like data storage and OOP improvementswork on versioning (like Git/GitHub) and monitoring of training/predictionscode repository creation or efficiency
study the general concepts of the Machine Learning algorithm(s) used
understand the business problem and the Data Science solution
understand how often the model would need to be trained, tested, and deployed
how many predictions will you make and when?
but more importantly (the Data Scientists can work on steps 2–4 as well), see how you can use your expertise to automate the whole workflow
also, to implement the model within the app or software of your company (e.g., inserting model results hourly into a table that shows into a UI that a consumer sees)
ultimately optimize the model itself with Data Engineering techniques like data storage and OOP improvements
work on versioning (like Git/GitHub) and monitoring of training/predictions
code repository creation or efficiency
As you can see, some of the steps of the overall Data Science process overlap between Data Scientists and MLOps Engineers. They often work closely together to achieve the ultimate goal of the company running a beneficial Machine Learning algorithm or Data Science model.
You can expect these roles to be different from company to company, but, in general, there are key similarities and differences between these two positions that can mostly be applied anywhere. If you are capable of performing both, then great for you, but at some point, the scalable method will require both Data Science and MLOps Engineering to be working together — by more than one person.
Here are the similarities that you can expect between the two roles
both need to understand the business, problem, and solution (at least a high-level overview)
both need to know the data of the company well and where to look for more if needed
both usually are proficient in SQL and Python
both usually work with Git and GitHub (version control and repositories)
both need to know the concept of training and testing
Here are the differences that you can expect between the two roles
Data Scientists usually work or develop in their Jupyter Notebooks or something similar
Data Scientists tend to be more research-oriented whereas...
MLOps focus on production-ready code and programming
MLOps work with DevOp tools like Docker and CircleCi
as well as with AWS/EC2, Google Cloud, or Kubeflow
MLOps tend to focus more on OOP
Data Scientists must know how the actual Machine Learning algorithm works (e.g., gradient descent, regularizations, parameter tuning, etc.)
Data Scientists focus on choosing and creating the algorithm (e.g., is it supervised, is it unsupervised, is it regression, is it classification?)
Schooling/education is different. Usually, a Masters in Data Science for Data Scientists and a Software Engineering Bachelors for MLOps Engineers — more and more undergrad schools are creating Data Science Bachelor’s degrees.
It is also beneficial to have specialization in Software Engineering for Data Scientists, and Machine Learning for MLOps, in terms of certifications or other forms of shorter educational experiences so that both roles are more well-rounded and can collaborate together better.
Above are just some of the key similarities and differences between Data Scientists and Machine Learning Operations Engineers. There are much more, and if you are currently in one of these roles you may experience something similar or different to what I described. Overall, if you are in a different role and are choosing between these two, you cannot go wrong. It ultimately depends on your preferences and what you excel in. For example, I would summarize Data Science as statistics, Machine Learning, and business analysis, and for Machine Learning Operations Engineering, I would summarize the role as a combination of Software Engineering, Machine Learning deployment expertise, Data Engineering, and DevOps. Data Scientists focus on the algorithms at hand, whereas MLOps works on the deployment and automation of the algorithm.
As you can see, both roles require a few different skills and have different goals, but at the same time, they share a plethora of skills, and ultimately the main goal is the same. I personally like the role of Data Scientist more than MLOps, however, I find myself learning more and more MLOps tools and practices every day. I do strive to be aware of all that an MLOps person would know so that I can slowly become more efficient (and it is interesting). Both positions are highly critical to the company, so if you want to have a positive impact on your company, then pursuing one of these roles would be a great idea.
Overall, here are both roles summarized
Data Scientists: business analysis, research, data, statistics, and Machine Learning algorithmsMLOps Engineer: programming, Software Engineering, productionaliztion, DevOps, and automation
I hope you found this article both interesting and useful. Keep in mind this article is based on my opinion and personal experiences with both roles. If you disagree or agree, feel free to comment down below why and the specific things you would add. Do you like being a Data Scientist more, or an MLOps Engineer more? Do you think they should be consolidated into one role? Is there really a difference? It would be interesting to receive some insight from others so that everyone can learn from others in order to find out the best representation of the similarities and differences between Data Science and Machine Learning Operations Engineering. Thank you for reading and feel free to check out my profile or read other articles and contact me if you have any questions about any of them.
Here is another article I have written covering Data Science versus Business Analytics [5]:
towardsdatascience.com
[1] Photo by LinkedIn Sales Navigator on Unsplash, (2020)
[2] Photo by Álvaro Bernal on Unsplash, (2019)
[3] Photo by Jefferson Santos on Unsplash, (2017)
[4] Photo by Veronica Benavides on Unsplash, (2017)
[5] M.Przybyla, Data Scientist vs Business Analyst. Here’s the Difference, (2020