Machine learning engineer
A machine learning engineer is a practitioner who develops, trains, deploys, operates, and improves machine-learning models and systems.
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.
- How it is used
- Assigns responsibility for model infrastructure, testing systems, and evaluations within an ML project team.
- 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.
Machine Learning System Design
A software-oriented professional who develops, deploys, operates, and improves machine-learning systems.
- How it is used
- Frames ML engineering through the broad responsibilities required to design and operate machine-learning systems.
- How deep it goes
- Defines the role through contrasting responsibilities but stops short of prescribing a standardized job description.
- Examples
- Uses pivot tables, distributed clusters, computer vision, chatbots, recommender systems, SQL, dashboards, and A/B tests.
- What you can do after
- Evaluate ML engineering roles by comparing their technical, operational, analytical, and stakeholder responsibilities.
Read how Machine Learning System Design explains machine learning engineer →
The Art of AI Product Development
A machine learning engineer is a practitioner who designs, trains, optimizes, deploys, and monitors machine-learning models and systems.
- How it is used
- Coordinates model development and system integration so machine learning capabilities can fulfill user needs.
- How deep it goes
- Defines responsibilities, skills, and role boundaries but does not detail implementation procedures or model architectures.
- Examples
- Uses a lean team, model training, evaluation, deployment, and shared MLOps infrastructure as concrete examples.
- What you can do after
- Build machine learning systems and manage their training, evaluation, integration, deployment, and supporting infrastructure.
Read how The Art of AI Product Development explains machine learning engineer →
Related concepts
- language modelimplements · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.
- Model Servingimplements · A model-serving platform is software infrastructure that hosts trained models and exposes inference by accepting requests and returning model outputs.
- DevOps engineeralternative to · An engineer who builds, automates, and operates software delivery processes and production infrastructure.
- mlops engineeruses · An MLOps engineer operates and automates the infrastructure and workflows supporting machine-learning systems throughout their lifecycle.
- machine learning projectused by · A machine-learning project is an organized effort to design, build, evaluate, deploy, and maintain a system containing machine-learned components.
These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.