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Machine Learning Engineer vs. Data Scientist: Which is Better?
Data Science and machine learning are the trending fields in current business scenarios, where almost all kinds of product and service-based companies are leveraging Machine learning and data science techniques to enhance their productivity and advance their workflows.
In such cases, many data aspirants are trying to enter the field, but the issue here is with the role. As one single individual can not master all the fields in AI and hence the need for selection of roles comes, which becomes very confusing but important for the career.
In this article, we will discuss the machine learning and data scientist role, which role is suitable for what kind of people, what key skills are required, what are the key responsibilities of the particular roles, and finally, the comparison between job opportunities between the two roles.
Machine Learning Engineer
As we know that machine learning and data science techniques target to predict or forecast future data observations on the basis of the current or past data available. Machine learning engineers are the professionals who build and deploy the model by training it with the already available data.
They are the professionals who are responsible for deciding, designing, training, building, and deploying the machine learning model. Also, in case of any problem with the model, they are required to solve the same and maintain the models on a daily basis.
The machine learning engineers should have strong command over one of the programming skills, as it is very base level to learn and have an idea about to build and deploy the machine learning models. The engineers should be able to code efficiently in languages like Python, R, C++, or Java.
Machine Learning Frameworks
As the machine learning engineers are the ones who develop and deploy the model, it is necessary for them to have an idea about the machine learning frameworks, how they work, which is better for what type of case, etc.
Machine Learning Algorithms
Machine learning algorithms are one of the key skills to have for machine learning engineers as they are supposed to perform try-and-error operations on live deployed models. Hence they should be aware of the algorithms which are being used and best case algorithms for the particular problem statement.
The machine learning engineers should have an idea about the cloud platforms like Google Cloud Platform (GCP), Microsoft Azure, Amazon Web Services (AWS), etc.
Machine learning engineers should also be good at communication and soft skills and should have the skills to convince and explain the working and problems of the model to the higher authorities and engineers working under him/her too.
The machine learning engineers are required to understand the current business problems and design a proper workflow of the model for the same.
Data preparation and feature engineering also come under machine learning engineers, who are required to design or transform the data according to the model.
The machine learning engineers are responsible for training the models on the already available dataset, whether it is too much data or limited data.
They are responsible for deploying the model efficiently in order to make it available for the business or people.
The machine learning engineers are also responsible for maintaining the model on a daily basis which is already deployed or trained.
Data scientists are professionals who use statistical and computational methods in order to extract valuable information and insights from the data. They are responsible for designing and implementing the data-driven solution for the business according to past records and conveying the same to the stack holders.
The data scientist should have strong programming skills in one or more languages in order to analyze and extract valuable insights from the data. They can have knowledge of programming languages like Python, R, or Java.
Statistics and Machine Learning
The data scientists should have an idea about the concepts in statistics and machine learning and should be able to apply the same while dealing with the data.
Data Analysis Skills
The data scientists are the one who extracts information from the data, and hence they should be familiar with the data analysis tools and libraries such as Matplotlib, Seaborn, Plotly, Tableau, and PowerBI.
The data scientist should have problems solving skills and should be able to think about the business problem and design an appropriate data-driven solution that is efficient and beneficial to the business.
The data scientists should have strong communication skills where they should be abler to communicate the workflow and designed data-driven solutions to the stack holder of the business and should be able to explain the complete process to the nontechnical people as well.
Data scientists are responsible for collecting and cleaning the data. They are required to collect the data without any error from the data engineering team or databases and clean the data according to the need of the model.
The data scientist is responsible for preprocessing the data, where the data is transformed into a better form in order to train the model.
The data scientists are responsible for conducting the analysis of the data, where the data is visualized, and key insights are taken note of.
The data scientists are responsible for designing the further workflow of the model, like which model will best suit the data and why to the machine learning engineering team.
The data scientist should be responsible for explaining the complete workflow of the model, from data collection to the model deployment to the higher authorities in the business.
Which is Better?
As machine learning and data scientists are both key professors in the field of artificial intelligence, it is tough to say which is better. However, one particular field can be best suitable and better for different kinds of people. Like, a person who has good data storytelling skills, analyzing skills, and statistical skills can go for a data scientist role, where it would be beneficial for the same.
On the other hand, a person with strong programming skills, model development skills, and algorithmic skills can go for machine learning engineer roles which would be better instead of data scientist roles.
In this article, we discussed the data scientist and machine learning engineers roles, with the key skills and responsibilities of the same. This article would help one to differentiate between these two orioles and would help one to decide on a particular role according to the key interest in the field.
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