Top 6 major differences between a data scientist and a machine learning engineer
This question has been asked so many times by enthusiasts, let's address it………
Data scientists and machine learning engineers are both responsible for developing and deploying machine learning models. However, they have different areas of focus and responsibilities which are often difficult to distinguish. Let’s take a look. Five clear summary distinctions are provided below, you may scroll straight to them, Enjoy!
Data scientist
Data scientists typically have a statistical background and a strong understanding of machine learning concepts. Their primary goal is to create models that can extract insights from data and predict outcomes. They oversee the entire model creation process, from data acquisition and cleaning to evaluating the model's performance and communicating the results to stakeholders. Exploratory data analysis, feature engineering, model selection and tuning, and interpreting the results to inform business decisions are part of their daily duties.
Machine learning (ML) engineer
Machine learning engineers on the other hand have a more technical background and focus on the practical application of machine learning models. They are in charge of developing the infrastructure and tools required to train, deploy, and monitor machine learning models in a production setting. This includes tasks like designing and implementing data pipelines, scaling model training and deployment, and monitoring model performance.
Data scientist vs machine learning engineer
To put it simply, a data scientist is more focused on the modelling and interpreting of data, while a machine learning engineer is more focused on the engineering and deployment of models. However, there are also many roles that combine elements of both, and the distinction between the two roles is not always clear-cut.
It's worth noticing that Data Scientist is a broader term and includes many sub-disciplines such as natural language processing, computer vision, speech recognition, and so on. As a result, a data scientist might have a more specific skillset in certain areas than a machine learning engineer, who has more general knowledge of creating and maintaining ML systems. For instance, I am more inclined to computer vision and natural language processing.
Differences between data scientists and machine learning engineers
Data Scientists Machine Learning Engineers
Data scientists focus on understanding the problem and identifying patterns in the data while ML engineers focus on designing and implementing systems to train and deploy models.
Data scientists are responsible for data cleaning, feature engineering, and model selection while ML engineers are responsible for designing and implementing data pipelines and scaling the training and deployment of models
Data scientists have a strong understanding of statistics and machine learning concepts while ML engineers have a strong understanding of computer science and software engineering concepts.
Data scientists communicate results and insights to stakeholders while ML engineers monitor and maintain deployed models in a production environment.
Data scientists spend more time on EDA, modelling, and interpreting results while ML engineers spend more time on feature extraction, data preprocessing, and model deployment.
Conclusion
As you may have seen from the content, the main difference is that data scientists are more focused on understanding the data, identifying patterns, and extracting insights from it. While machine learning engineers are more focused on the designing, development and maintenance of infrastructure that supports machine learning models, this includes designing and implementing data pipelines, scaling the training and deployment of models, monitoring the performance of deployed models and optimization of the system. Both roles are important and often work closely together to develop, improve, and deploy ML models effectively.
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