M
Master’s degree
- Length
- 36 credit hours, 12 courses, including an applied project drawn from your own workplace.
- Entry
- A bachelor’s degree or its equivalent, and professional experience related to the program.
- Status
- In preparation
- Levels
- Master's
- Credit hours
- 36
- Delivery
- Fully online
- Experience
- Assessed course by course
- Status
- In preparation
Overview
About the program
A master's program for developers and data staff who build or run models. You study the engineering around machine learning: data pipelines, training, deployment, monitoring and cost.
The Machine Learning Engineering program is designed for people who already work in the field. It covers data pipelines, model training and tuning, deployment and serving, monitoring and reliability, large language model applications, and ends with security and responsible use.
Like every Madison program, it is built for working adults. Courses you already master through your work are tested and recorded with a grade; the rest you study online, at your own pace, with tasks carried out in your own workplace.
In preparation. This program is not open for enrollment yet. It opens when its curriculum and courses are in place on the learning platform.
Outcomes
What you will be able to do
- 01Data pipelinesExplain the core ideas of data pipelines and use them to read real situations at work.
- 02Model training and tuningAnalyse a real problem in model training and tuning and recommend a course of action backed by evidence.
- 03Deployment and servingApply the methods of deployment and serving in your own organization and measure the result.
- 04Monitoring and reliabilityPlan and lead work in monitoring and reliability, and document your decisions to a professional standard.
- 05Large language model applicationsEvaluate current practice in large language model applications against recognised standards and propose improvements.
- 06Security and responsible useAdvise colleagues and managers on security and responsible use in clear, well-structured writing.
Curriculum
What you will study
The main areas of the curriculum. Your personal study plan lists the exact courses, their credit hours and those you may claim through assessment.
- 01Data pipelinesBuilding reliable flows of data from source to model.Core area
- 02Model training and tuningTraining, validating and tuning models for accuracy and robustness.Core area
- 03Deployment and servingPackaging models and serving predictions at scale.Core area
- 04Monitoring and reliabilityTracking model drift, performance and uptime after release.Core area
- 05Large language model applicationsPrompting, retrieval and building applications on language models.Core area
- 06Security and responsible useProtecting AI systems from misuse and using them responsibly.Core area
Levels
Levels, length and entry
Your experience
How your experience counts
- 01
Send your record
Your CV, certificates and evidence of work are mapped to the courses of this program.
- 02
Sit the course tests
Each course you claim is tested on its own. A pass is recorded on your transcript with its grade.
- 03
Study the rest
The remaining courses become your personal plan, with an expected end date in writing.
Professional titles
The field, by title
Beside the degree, this field is planned as a professional credential, with the title written before it. A title is not a degree. How titles work.
- 01Expert in Machine Learning EngineeringFor people who have practiced Machine Learning Engineering for years and want their competence assessed and recorded.
- 02Consultant in Machine Learning EngineeringFor experienced people who advise organizations on Machine Learning Engineering.
- 03Trainer in Machine Learning EngineeringFor experienced people who teach or coach others in Machine Learning Engineering at work.
- 04Diploma in Machine Learning EngineeringA focused credential in Machine Learning Engineering, based on assessed knowledge and evidence of practice.
In preparation. No title is open yet; the requirements are published when it opens.
Careers
Where this program is used
- Machine learning engineer
- MLOps engineer
- Data platform engineer
- AI technical lead
Roles this program relates to. A degree supports a career; it does not guarantee a position, and each employer sets its own requirements.