Categories: Education

What is the Difference Between Data Scientist and Machine Learning Ops Engineer?

Although there are many similarities between machine learning engineers and data scientists, their paths seem to differ early enough and that shows the difference they share. 

If we have to define the relationship between the two-

A data scientist has the background and skills using which he can perform statistical analysis and develop the ML initiative strategy along with the algorithm that’s provided to an ML model.

This is when the machine learning engineer has to use his skills of related software tools just to be sure that the models are scalable and functioning properly. 

Let’s learn more about the differences and similarities between these two outstanding careers.

Data Scientist

The role of a Data Scientist is no longer the same as it used to be; more and more specializations are emerging in the field of Data Science. A Data Scientist is known as a business-focused scientist who studies, finds, and resolves problems within the company with the help of Machine Learning algorithms. Check out the best Data Science Online Course to become a Data Scientist and help steer businesses to profits and newer opportunities. The main motive is to make a process accurate as well as more efficient as compared to before. It may be sounding like the role of a Software Engineer, however, it focuses more on the algorithms and how they work instead of focusing on more hand-made and object-oriented programming code solutions.

A Data Scientist role mainly involves the following responsibilities:

  1. research the data and products of your company
  2. meet with stakeholders to discuss pain points in the business
  3. Prepare a business problem statement that defines the issue to be resolved
  4. obtain the data usually with SQL or working with a Data Engineer
  5. Do exploratory data analysis on your selected dataset
  6. Compare several models to a baseline model
  7. Selecting your main algorithm
  8. Determine  key features
  9. Take off extra and superfluous features
  10. Create a stepwise algorithm process
  11. Save and test your model in a dev environment
  12. Give details on the accuracy
  13. Explain how you can make the product better

These steps in the Data Science process can update from time to time. Also, all businesses are different, still, if you follow a process of data creation, algorithm comparison, testing, and presentation of results, a great Data Scientist for your company is ready. It is after the last step when the role of MLOps Engineer comes into play. 

Mlops solutions prolongs the CRISP-DM methodology using an Agile approach along with technical tools for automated operations with data, ML models, code, and environment. You can implement MLOps to prevent common errors and problems faced by Data Scientists.

Machine Learning Operations Engineer

Referred to as an MLOps Engineer, the role of the Machine Learning Operations Engineer role is important as well as beneficial for the Data Science team. If you are currently working as a Software Engineer or Data Scientist with knowledge of the way algorithms work and want to work cross-functionally with Machine Learning algorithms or want to focus more on the Software Engineering, Data Engineering, and deployment of models, you might see yourself switching to the role of Machine Learning Operations Engineer.

While working as an MLOps Engineer, you will first be given a Data Science model that has been developed by a Data Scientist. Apart from that, you will get to work on seeing how to optimize some Data Science code.

An MLOps Engineer role involves the following responsibilities:

  1. Understanding the general concepts of the Machine Learning algorithm(s)
  2. Examining the business problem and the Data Science solution
  3. Understanding how often you will need to train, test or deploy the model.
  4. The number of predictions to be made and when
  5. How you use your expertise
  6. Carry out the model within your company’s app or software
  7. Optimize the model with Data Engineering techniques

Differences Between Data Scientist vs Machine Learning Ops Engineer

Here are the differences between the two roles:

  1. Data Scientists are more research-oriented whereas MLOps mainly focus on production-ready code and programming
  2. MLOps focus more on OOP and work with DevOp tools like Docker and CircleCi
  3. Data Scientists are proficient in working with the actual Machine Learning algorithm
  4. Data Scientists are responsible for choosing and creating the algorithm
  5. There is also a difference in their education. Usually, there is a Master’s Degree in Data Science for Data Scientists whereas a Software Engineering Bachelors for MLOps Engineers.
  6. You can also have specialization in Software Engineering for Data Scientists, and Machine Learning for MLOps, such as certifications or you can go for shorter educational experiences so that you can collaborate both roles together better.

So that was everything about the two job roles and how are they diverse from each other.

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