Many individuals find themselves a bit confused between a data scientist and a machine learning engineer. This is because these are two exceptionally imperative roles that many organizations are taking the aid of. This article gives an outline of the distinction between these two profiles.
A machine learning engineer is a professional with the skills and abilities to develop, maintain and optimize algorithms. These algorithms are trained by these experts to solve various data-related problems.
They employ gigantic data sets to help in building models, which are capable of predicting future outcomes or events. Their job entails working with software developers and data scientists.
Related Article- Machine Learning Operations
A data scientist is a professional with knowledge of employing statistical methods, data mining, predictive analytics and machine learning. All these skills and knowledge are used to convert raw data into highly actionable insights for an organization.
These professionals are in high demand throughout many domains and industries like finance, healthcare and banking. Their skills in identifying anomalies, trends, and patterns make them an asset to every organization.
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Here is a comparative table to better understand the different roles and responsibilities of a data scientist and a machine learning engineer.
| DATA SCIENTIST | MACHINE LEARNING ENGINEER |
| They identify & validate business processes that are solvable by using ML. | Their job is to deploy deep learning and ML models to production. |
| They must develop custom models and algorithms. | They carry out inference testing on many different types of hardware. This includes GPU, edge devices & CPU. |
| They are in charge of developing data annotation strategies. | They are in charge of version control of experiments, metadata & models. |
| They must analyze & visualize data at various stages of the machine learning lifecycle. | They must optimize models for better memory, performance, throughput & latency. |
| It's their job to coordinate with cross-functional stakeholders. | It's their job to develop custom tools for optimizing the complete deployment workflow. |
| They identify generative synthetic data & additional datasets. | They take care of debugging, model performance & maintenance. |
Here is a distinction in these two profiles on the basis of the required skill set.
| DATA SCIENTIST | MACHINE LEARNING ENGINEER |
| Programming | Programming |
| Data science | Data modelling |
| Data analytics | Conceptual knowledge of ML |
| Presentation skills | Data structures |
| Problem-solving | Software engineering |
| ML - supervised & unsupervised | ML frameworks |
| Statistics | Statistics |
| Written & oral communication skills | |
| Data visualization |
Both data scientist vs machine learning engineer are profiles that require great technical prowess. Here is a table talking about the tech stack knowledge needed to become a pro at the respective profiles.
| DATA SCIENTIST | MACHINE LEARNING ENGINEER |
| Git, Github, Bitbucket | Git, Github, Bitbucket |
| Data science | Data modelling |
| Python/ SQL/ R | Python / Scala / C++ |
| ML - Rapids, Scikit-learn, Fast.ai | DL - TensorFlow, PyTorch, MXNet, JAX |
| Google Colab notebooks, Jupyter, SageMaker | Bash, Linux |
| Visualization - Seaborn, Matplotlib, Bokeh | Serving - TensorRT, ONNX, TFServing, TorchServe |
| Cloud - GCP/ AWS/ Azure | Cloud - GCP/ AWS/ Azure |
| Spark | Kubernetes, Docker |
Both Machine Learning engineers and data scientists are in high demand. Each of these professions are ones that will rule the future and choosing to step into either of these is highly beneficial.
You can also read: DevOps Interview Questions with Answers
It depends on your interests: data scientists focus on data analysis and insights, while machine learning engineers focus model development and deployment. They both play an important role.
Yes, a data scientist can do a machine learning engineer role but with additional focus on software engineering and model deployment skills.
Course Schedule
| Course Name | Batch Type | Details |
| Machine Learning Training | Every Weekday | View Details |
| Machine Learning Training | Every Weekend | View Details |
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