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Machine learning engineer

As explained in Delivering Machine Learning Projects: From design to deployment

A machine learning engineer is a specialist who develops, integrates, tests, and operates software systems containing machine learning models.

A machine learning engineer works with the models an ML project produces. The role includes creating and using infrastructure to store, version, and manage models, setting up testing systems, and running evaluations. section 3.2.2

Staff this role against the work plan rather than assuming the same team is needed throughout. In one project plan, two ML engineers join a data scientist for the earlier work; one ML engineer stays for the final four weeks as UX and application development roles join. section 4.9

How Delivering Machine Learning Projects: From design to deployment covers it

How deep it goes
Defines the role through its tasks but does not detail how to implement model storage, testing, or evaluation.
Examples
Uses a project staffing plan that retains one of two ML engineers for the final four weeks.
What you can do after
Practice/perform it: plan ML engineer assignments around model infrastructure, testing, evaluation, and changing project staffing needs.
How it connects
Connects the role to data scientists, data engineers, model versioning, testing, and evaluation.

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This explainer was generated by Manning's knowledge graph from the text of Delivering Machine Learning Projects: From design to deployment. The section links point to the parts of the book it draws on.