Jobs similar to a machine learning engineer role in a data science team include data scientists, data engineers and AI engineers.
Machine learning engineer vs data scientist
While the functions of a machine learning engineer and data scientist may overlap, these specialists are responsible for different parts of an ML project. Data scientists follow the data science process, they carry out experiments to understand data and build models. Then they evaluate the model and make sure it meets the desired outcome of the project before handing it over to ML engineers.
Machine learning engineers are tech specialists who create and maintain ML infrastructure upon which models are deployed to a production environment. They take the models built by data scientists and make them work in a production environment where the model will be accessible to your computers, phones and other software systems.
Machine learning engineer vs data engineer
While machine learning engineers are responsible for delivering the ready-to-use AI models, data engineers design the whole data architecture and the application logic to process the data. In other words, data engineers are specialized in data pipelines converting raw data into a useful format for analysis and making sure that data flows as required for the models to actually work.
Machine learning engineer vs AI engineer
AI engineers develop and program algorithms that make up machines capable of functioning like a human brain. The role requires a sound understanding of programming, software engineering, data science and data engineering. They build and test AI models and then use API calls or embedded code to create AI applications.
Whether you’re just starting to explore or have already committed to a career in machine learning, we hope this guide will help you get a better understanding of a machine learning engineer role and move forward in this field.