MSc

MSc AI Product Management

MSc AI Product Management — an accredited Master's for people doing this work, not just reading about it.

Online only

18 months36 creditsEnglish (ES/PT/HI support hubs do not change the language of assessment unless a bilingual pathway is stated)

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Overview

Building products on machine learning — evaluation, data strategy, human-in-the-loop design, cost and the responsible-AI constraints product managers must hold. GlobalMind University delivers this award fully online across six world regions. There is no campus gate. Assessment is identity-checked and one academic standard applies to every student, in every time zone. This is an academic Master's award; where a field has a licensing or registration route, that remains a matter for your professional body or regulator. You will work through the material as a practitioner-researcher: applied methods, real or anonymised cases, and a supervised capstone or dissertation as the final component. Faculty mark whether you noticed the constraints a slide deck usually hides. MSc AI Product Management is taught as one award, not a list of unrelated modules. The published length is 18 months, and the credit total is 36. Students move from Mathematics & Probability for AI through Mathematics & Probability for AI; Machine Learning Theory & Practice; Programming Intelligent Systems; Deep Learning & Representation Learning; Natural Language Processing & Large Language Models; Computer Vision & Multimodal AI, and finish on AI Capstone Project. Each course has a question, a method, and a submission. In this field, students define a measurable question, fit a method that can be checked, and report where it fails. Reading is a dataset description, a methods note, and one published evaluation. The artefact a marker expects is a project note with the question, the method, the result, and the limitation. The award is built so that a graduate can do the following in practice: Build and evaluate machine-learning models against a real objective Judge where AI is and is not an appropriate solution, and say why Take a model from notebook to a monitored production service Teaching assumes the student can read a source, attempt a problem before the seminar, and revise after feedback. Attendance at live seminars is part of the design. The capstone or final course must use the methods of the earlier courses; a project that ignores them does not pass. The pages for each course name the topics that are examined. Those topics are the syllabus. A brochure line is not a substitute for them.

The programme carries 36 credits across four assessed modules. Delivery is tuition-free aside from the examination fee. Transcripts use letter grades and a GPA scale consistent with GlobalMind University awards. No professional-body logo is used in marketing to imply endorsement.

Who This Programme Is For

Software engineers and analysts moving into machine learning
Product managers who must own an ML-backed product
Public-sector and regulated-industry staff commissioning AI
Graduates in a quantitative discipline heading for research

How you can study

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Tuition is free. Examinations are free. There is no exam fee. Study from anywhere in the world.

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What You Will Learn

Build and evaluate machine-learning models against a real objective
Judge where AI is and is not an appropriate solution, and say why
Take a model from notebook to a monitored production service
Hold a responsible-AI line on data, bias, evaluation and cost

Curriculum

Module 1: Foundations of Intelligent Systems

Code	Course	Credits · hours
GX72201	Mathematics & Probability for AI Mathematics & Probability for AI (GX72201) is a 3-credit course on MSc AI Product Management, with 30 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Mathematics & Probability for AI as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone. GX72201-M1 · The question, the unit of analysis, and data quality — Mathematics & Probability for AI GX72201-M2 · Features, baselines, and error — Mathematics & Probability for AI GX72201-M3 · Validation, leakage, and what the model must not decide — Mathematics & Probability for AI GX72201-M4 · Monitoring, drift, and human review — Mathematics & Probability for AI GX72201-M5 · Interpretation for a non-technical decision maker — Mathematics & Probability for AI GX72201-M6 · A supervised project with method, result, and limitation — Mathematics & Probability for AI	3 · 30 h
GX72202	Machine Learning Theory & Practice Machine Learning Theory & Practice (GX72202) is a 3-credit course on MSc AI Product Management, with 30 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Machine Learning Theory & Practice as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone. GX72202-M1 · The question, the unit of analysis, and data quality — Machine Learning Theory & Practice GX72202-M2 · Features, baselines, and error — Machine Learning Theory & Practice GX72202-M3 · Validation, leakage, and what the model must not decide — Machine Learning Theory & Practice GX72202-M4 · Monitoring, drift, and human review — Machine Learning Theory & Practice GX72202-M5 · Interpretation for a non-technical decision maker — Machine Learning Theory & Practice GX72202-M6 · A supervised project with method, result, and limitation — Machine Learning Theory & Practice	3 · 30 h
GX72203	Programming Intelligent Systems Programming Intelligent Systems (GX72203) is a 3-credit course on MSc AI Product Management, with 30 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Programming Intelligent Systems as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone. GX72203-M1 · The question, the unit of analysis, and data quality — Programming Intelligent Systems GX72203-M2 · Features, baselines, and error — Programming Intelligent Systems GX72203-M3 · Validation, leakage, and what the model must not decide — Programming Intelligent Systems GX72203-M4 · Monitoring, drift, and human review — Programming Intelligent Systems GX72203-M5 · Interpretation for a non-technical decision maker — Programming Intelligent Systems GX72203-M6 · A supervised project with method, result, and limitation — Programming Intelligent Systems	3 · 30 h

Code

Course

Credits · hours

GX72201

Mathematics & Probability for AI

Mathematics & Probability for AI (GX72201) is a 3-credit course on MSc AI Product Management, with 30 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Mathematics & Probability for AI as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.

GX72201-M1 · The question, the unit of analysis, and data quality — Mathematics & Probability for AI
GX72201-M2 · Features, baselines, and error — Mathematics & Probability for AI
GX72201-M3 · Validation, leakage, and what the model must not decide — Mathematics & Probability for AI
GX72201-M4 · Monitoring, drift, and human review — Mathematics & Probability for AI
GX72201-M5 · Interpretation for a non-technical decision maker — Mathematics & Probability for AI
GX72201-M6 · A supervised project with method, result, and limitation — Mathematics & Probability for AI

3 · 30 h

GX72202

Machine Learning Theory & Practice

Machine Learning Theory & Practice (GX72202) is a 3-credit course on MSc AI Product Management, with 30 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Machine Learning Theory & Practice as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.

GX72202-M1 · The question, the unit of analysis, and data quality — Machine Learning Theory & Practice
GX72202-M2 · Features, baselines, and error — Machine Learning Theory & Practice
GX72202-M3 · Validation, leakage, and what the model must not decide — Machine Learning Theory & Practice
GX72202-M4 · Monitoring, drift, and human review — Machine Learning Theory & Practice
GX72202-M5 · Interpretation for a non-technical decision maker — Machine Learning Theory & Practice
GX72202-M6 · A supervised project with method, result, and limitation — Machine Learning Theory & Practice

3 · 30 h

GX72203

Programming Intelligent Systems

Programming Intelligent Systems (GX72203) is a 3-credit course on MSc AI Product Management, with 30 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Programming Intelligent Systems as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.

GX72203-M1 · The question, the unit of analysis, and data quality — Programming Intelligent Systems
GX72203-M2 · Features, baselines, and error — Programming Intelligent Systems
GX72203-M3 · Validation, leakage, and what the model must not decide — Programming Intelligent Systems
GX72203-M4 · Monitoring, drift, and human review — Programming Intelligent Systems
GX72203-M5 · Interpretation for a non-technical decision maker — Programming Intelligent Systems
GX72203-M6 · A supervised project with method, result, and limitation — Programming Intelligent Systems

3 · 30 h

Module 2: Advanced Methods

Code	Course	Credits · hours
GX72205	Deep Learning & Representation Learning Deep Learning & Representation Learning (GX72205) is a 3-credit course on MSc AI Product Management, with 30 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Deep Learning & Representation Learning as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone. GX72205-M1 · The question, the unit of analysis, and data quality — Deep Learning & Representation Learning GX72205-M2 · Features, baselines, and error — Deep Learning & Representation Learning GX72205-M3 · Validation, leakage, and what the model must not decide — Deep Learning & Representation Learning GX72205-M4 · Monitoring, drift, and human review — Deep Learning & Representation Learning GX72205-M5 · Interpretation for a non-technical decision maker — Deep Learning & Representation Learning GX72205-M6 · A supervised project with method, result, and limitation — Deep Learning & Representation Learning	3 · 30 h
GX72206	Natural Language Processing & Large Language Models Natural Language Processing & Large Language Models (GX72206) is a 3-credit course on MSc AI Product Management, with 30 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Natural Language Processing & Large Language Models as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone. GX72206-M1 · The question, the unit of analysis, and data quality — Natural Language Processing & Large Language Models GX72206-M2 · Features, baselines, and error — Natural Language Processing & Large Language Models GX72206-M3 · Validation, leakage, and what the model must not decide — Natural Language Processing & Large Language Models GX72206-M4 · Monitoring, drift, and human review — Natural Language Processing & Large Language Models GX72206-M5 · Interpretation for a non-technical decision maker — Natural Language Processing & Large Language Models GX72206-M6 · A supervised project with method, result, and limitation — Natural Language Processing & Large Language Models	3 · 30 h
GX72207	Computer Vision & Multimodal AI Computer Vision & Multimodal AI (GX72207) is a 3-credit course on MSc AI Product Management, with 30 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Computer Vision & Multimodal AI as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone. GX72207-M1 · The question, the unit of analysis, and data quality — Computer Vision & Multimodal AI GX72207-M2 · Features, baselines, and error — Computer Vision & Multimodal AI GX72207-M3 · Validation, leakage, and what the model must not decide — Computer Vision & Multimodal AI GX72207-M4 · Monitoring, drift, and human review — Computer Vision & Multimodal AI GX72207-M5 · Interpretation for a non-technical decision maker — Computer Vision & Multimodal AI GX72207-M6 · A supervised project with method, result, and limitation — Computer Vision & Multimodal AI	3 · 30 h

Code

Course

Credits · hours

GX72205

Deep Learning & Representation Learning

Deep Learning & Representation Learning (GX72205) is a 3-credit course on MSc AI Product Management, with 30 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Deep Learning & Representation Learning as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.

GX72205-M1 · The question, the unit of analysis, and data quality — Deep Learning & Representation Learning
GX72205-M2 · Features, baselines, and error — Deep Learning & Representation Learning
GX72205-M3 · Validation, leakage, and what the model must not decide — Deep Learning & Representation Learning
GX72205-M4 · Monitoring, drift, and human review — Deep Learning & Representation Learning
GX72205-M5 · Interpretation for a non-technical decision maker — Deep Learning & Representation Learning
GX72205-M6 · A supervised project with method, result, and limitation — Deep Learning & Representation Learning

3 · 30 h

GX72206

Natural Language Processing & Large Language Models

Natural Language Processing & Large Language Models (GX72206) is a 3-credit course on MSc AI Product Management, with 30 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Natural Language Processing & Large Language Models as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.

GX72206-M1 · The question, the unit of analysis, and data quality — Natural Language Processing & Large Language Models
GX72206-M2 · Features, baselines, and error — Natural Language Processing & Large Language Models
GX72206-M3 · Validation, leakage, and what the model must not decide — Natural Language Processing & Large Language Models
GX72206-M4 · Monitoring, drift, and human review — Natural Language Processing & Large Language Models
GX72206-M5 · Interpretation for a non-technical decision maker — Natural Language Processing & Large Language Models
GX72206-M6 · A supervised project with method, result, and limitation — Natural Language Processing & Large Language Models

3 · 30 h

GX72207

Computer Vision & Multimodal AI

Computer Vision & Multimodal AI (GX72207) is a 3-credit course on MSc AI Product Management, with 30 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Computer Vision & Multimodal AI as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.

GX72207-M1 · The question, the unit of analysis, and data quality — Computer Vision & Multimodal AI
GX72207-M2 · Features, baselines, and error — Computer Vision & Multimodal AI
GX72207-M3 · Validation, leakage, and what the model must not decide — Computer Vision & Multimodal AI
GX72207-M4 · Monitoring, drift, and human review — Computer Vision & Multimodal AI
GX72207-M5 · Interpretation for a non-technical decision maker — Computer Vision & Multimodal AI
GX72207-M6 · A supervised project with method, result, and limitation — Computer Vision & Multimodal AI

3 · 30 h

Module 3: Systems & Governance

Code	Course	Credits · hours
GX72209	MLOps, Evaluation & Production AI MLOps, Evaluation & Production AI (GX72209) is a 3-credit course on MSc AI Product Management, with 30 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using MLOps, Evaluation & Production AI as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone. GX72209-M1 · The question, the unit of analysis, and data quality — MLOps, Evaluation & Production AI GX72209-M2 · Features, baselines, and error — MLOps, Evaluation & Production AI GX72209-M3 · Validation, leakage, and what the model must not decide — MLOps, Evaluation & Production AI GX72209-M4 · Monitoring, drift, and human review — MLOps, Evaluation & Production AI GX72209-M5 · Interpretation for a non-technical decision maker — MLOps, Evaluation & Production AI GX72209-M6 · A supervised project with method, result, and limitation — MLOps, Evaluation & Production AI	3 · 30 h
GX72210	Responsible AI, Safety & Policy Responsible AI, Safety & Policy (GX72210) is a 3-credit course on MSc AI Product Management, with 30 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Responsible AI, Safety & Policy as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone. GX72210-M1 · The question, the unit of analysis, and data quality — Responsible AI, Safety & Policy GX72210-M2 · Features, baselines, and error — Responsible AI, Safety & Policy GX72210-M3 · Validation, leakage, and what the model must not decide — Responsible AI, Safety & Policy GX72210-M4 · Monitoring, drift, and human review — Responsible AI, Safety & Policy GX72210-M5 · Interpretation for a non-technical decision maker — Responsible AI, Safety & Policy GX72210-M6 · A supervised project with method, result, and limitation — Responsible AI, Safety & Policy	3 · 30 h
GX72211	Cost, Scale & Infrastructure Cost, Scale & Infrastructure (GX72211) is a 3-credit course on MSc AI Product Management, with 30 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Cost, Scale & Infrastructure as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone. GX72211-M1 · The question, the unit of analysis, and data quality — Cost, Scale & Infrastructure GX72211-M2 · Features, baselines, and error — Cost, Scale & Infrastructure GX72211-M3 · Validation, leakage, and what the model must not decide — Cost, Scale & Infrastructure GX72211-M4 · Monitoring, drift, and human review — Cost, Scale & Infrastructure GX72211-M5 · Interpretation for a non-technical decision maker — Cost, Scale & Infrastructure GX72211-M6 · A supervised project with method, result, and limitation — Cost, Scale & Infrastructure	3 · 30 h

Code

Course

Credits · hours

GX72209

MLOps, Evaluation & Production AI

MLOps, Evaluation & Production AI (GX72209) is a 3-credit course on MSc AI Product Management, with 30 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using MLOps, Evaluation & Production AI as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.

GX72209-M1 · The question, the unit of analysis, and data quality — MLOps, Evaluation & Production AI
GX72209-M2 · Features, baselines, and error — MLOps, Evaluation & Production AI
GX72209-M3 · Validation, leakage, and what the model must not decide — MLOps, Evaluation & Production AI
GX72209-M4 · Monitoring, drift, and human review — MLOps, Evaluation & Production AI
GX72209-M5 · Interpretation for a non-technical decision maker — MLOps, Evaluation & Production AI
GX72209-M6 · A supervised project with method, result, and limitation — MLOps, Evaluation & Production AI

3 · 30 h

GX72210

Responsible AI, Safety & Policy

Responsible AI, Safety & Policy (GX72210) is a 3-credit course on MSc AI Product Management, with 30 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Responsible AI, Safety & Policy as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.

GX72210-M1 · The question, the unit of analysis, and data quality — Responsible AI, Safety & Policy
GX72210-M2 · Features, baselines, and error — Responsible AI, Safety & Policy
GX72210-M3 · Validation, leakage, and what the model must not decide — Responsible AI, Safety & Policy
GX72210-M4 · Monitoring, drift, and human review — Responsible AI, Safety & Policy
GX72210-M5 · Interpretation for a non-technical decision maker — Responsible AI, Safety & Policy
GX72210-M6 · A supervised project with method, result, and limitation — Responsible AI, Safety & Policy

3 · 30 h

GX72211

Cost, Scale & Infrastructure

Cost, Scale & Infrastructure (GX72211) is a 3-credit course on MSc AI Product Management, with 30 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Cost, Scale & Infrastructure as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.

GX72211-M1 · The question, the unit of analysis, and data quality — Cost, Scale & Infrastructure
GX72211-M2 · Features, baselines, and error — Cost, Scale & Infrastructure
GX72211-M3 · Validation, leakage, and what the model must not decide — Cost, Scale & Infrastructure
GX72211-M4 · Monitoring, drift, and human review — Cost, Scale & Infrastructure
GX72211-M5 · Interpretation for a non-technical decision maker — Cost, Scale & Infrastructure
GX72211-M6 · A supervised project with method, result, and limitation — Cost, Scale & Infrastructure

3 · 30 h

Module 4: Integration & Capstone

Code	Course	Credits · hours
GX72213	Research Methods for Applied AI Research Methods for Applied AI (GX72213) is a 3-credit course on MSc AI Product Management, with 30 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Research Methods for Applied AI as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone. GX72213-M1 · The question, the unit of analysis, and data quality — Research Methods for Applied AI GX72213-M2 · Features, baselines, and error — Research Methods for Applied AI GX72213-M3 · Validation, leakage, and what the model must not decide — Research Methods for Applied AI GX72213-M4 · Monitoring, drift, and human review — Research Methods for Applied AI GX72213-M5 · Interpretation for a non-technical decision maker — Research Methods for Applied AI GX72213-M6 · A supervised project with method, result, and limitation — Research Methods for Applied AI	3 · 30 h
GX72214	Communicating AI to Non-Specialists Communicating AI to Non-Specialists (GX72214) is a 3-credit course on MSc AI Product Management, with 30 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Communicating AI to Non-Specialists as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone. GX72214-M1 · The question, the unit of analysis, and data quality — Communicating AI to Non-Specialists GX72214-M2 · Features, baselines, and error — Communicating AI to Non-Specialists GX72214-M3 · Validation, leakage, and what the model must not decide — Communicating AI to Non-Specialists GX72214-M4 · Monitoring, drift, and human review — Communicating AI to Non-Specialists GX72214-M5 · Interpretation for a non-technical decision maker — Communicating AI to Non-Specialists GX72214-M6 · A supervised project with method, result, and limitation — Communicating AI to Non-Specialists	3 · 30 h
GX72215	AI Capstone Project AI Capstone Project (GX72215) is a 3-credit course on MSc AI Product Management, with 30 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using AI Capstone Project as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone. GX72215-M1 · The question, the unit of analysis, and data quality — AI Capstone Project GX72215-M2 · Features, baselines, and error — AI Capstone Project GX72215-M3 · Validation, leakage, and what the model must not decide — AI Capstone Project GX72215-M4 · Monitoring, drift, and human review — AI Capstone Project GX72215-M5 · Interpretation for a non-technical decision maker — AI Capstone Project GX72215-M6 · A supervised project with method, result, and limitation — AI Capstone Project	3 · 30 h

Code

Course

Credits · hours

GX72213

Research Methods for Applied AI

Research Methods for Applied AI (GX72213) is a 3-credit course on MSc AI Product Management, with 30 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Research Methods for Applied AI as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.

GX72213-M1 · The question, the unit of analysis, and data quality — Research Methods for Applied AI
GX72213-M2 · Features, baselines, and error — Research Methods for Applied AI
GX72213-M3 · Validation, leakage, and what the model must not decide — Research Methods for Applied AI
GX72213-M4 · Monitoring, drift, and human review — Research Methods for Applied AI
GX72213-M5 · Interpretation for a non-technical decision maker — Research Methods for Applied AI
GX72213-M6 · A supervised project with method, result, and limitation — Research Methods for Applied AI

3 · 30 h

GX72214

Communicating AI to Non-Specialists

Communicating AI to Non-Specialists (GX72214) is a 3-credit course on MSc AI Product Management, with 30 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Communicating AI to Non-Specialists as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.

GX72214-M1 · The question, the unit of analysis, and data quality — Communicating AI to Non-Specialists
GX72214-M2 · Features, baselines, and error — Communicating AI to Non-Specialists
GX72214-M3 · Validation, leakage, and what the model must not decide — Communicating AI to Non-Specialists
GX72214-M4 · Monitoring, drift, and human review — Communicating AI to Non-Specialists
GX72214-M5 · Interpretation for a non-technical decision maker — Communicating AI to Non-Specialists
GX72214-M6 · A supervised project with method, result, and limitation — Communicating AI to Non-Specialists

3 · 30 h

GX72215

AI Capstone Project

AI Capstone Project (GX72215) is a 3-credit course on MSc AI Product Management, with 30 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using AI Capstone Project as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.

GX72215-M1 · The question, the unit of analysis, and data quality — AI Capstone Project
GX72215-M2 · Features, baselines, and error — AI Capstone Project
GX72215-M3 · Validation, leakage, and what the model must not decide — AI Capstone Project
GX72215-M4 · Monitoring, drift, and human review — AI Capstone Project
GX72215-M5 · Interpretation for a non-technical decision maker — AI Capstone Project
GX72215-M6 · A supervised project with method, result, and limitation — AI Capstone Project

3 · 30 h

Career Paths

Machine-learning engineer
AI product manager
Applied research scientist
Data science lead
AI governance and assurance roles

Programme Highlights

Hands-on modelling, deployment and evaluation
Responsible-AI, safety and governance throughout
A capstone that ships a working, monitored model
No black-box theatre — you explain what the system does and does not do

Assessment

Modules are assessed through a published mix of coursework, applied projects, and examinations. Exam windows are announced in advance so students in other time zones are not forced into overnight sittings. Alternative arrangements are available where documented.

Admission requirements

Academic entry

Bachelor's degree from a recognised institution (minimum 2:2 / GPA 2.5/4.0 or an equivalent national scale)
Identity document for remote identity-checked assessment enrolment

English language

English proficiency: IELTS 5.5, TOEFL 46 or equivalent (waived for prior English-medium study). State your primary study mode without changing the academic standard.

Documents

Statement of Purpose (500 words minimum) covering goals, professional context and fit
Two academic or professional references with verifiable contact details
CV or résumé covering education and relevant work
Official transcripts (digital copies accepted at application; certified copies before award)

Programme questions

How long is the MSc AI Product Management?

The published duration is 18 months. Teaching language: English (ES/PT/HI support hubs do not change the language of assessment unless a bilingual pathway is stated). Actual time-to-complete depends on mode and any recognised prior learning.

Can I study this from my country?

This is a fully online award. You study from your country. No student visa and no campus relocation are required.

What does it cost?

Degree tuition for this award is published as £0 / tuition-free on the online pathway. Examination or administrative fees may apply at checkout — never an annual tuition invoice. Check the Fees page for any extras.

What are the entry requirements?

Requirements are grouped on this page (academic, English, documents). Equivalent qualifications are considered. English may be waived after prior English-medium study.

How is the programme assessed?

Assessment is typically a mix of coursework, projects, and examinations. Doctoral awards include a thesis or dissertation and an oral examination. Details sit in the programme specification and module outlines.

Will this award be recognised where I live?

Recognition of the award for local employment, professional licence, or ministry attestation is decided by your employer or regulator. GlobalMind University publishes verification pages for certificates. We do not claim automatic equivalence in every country.

How do I apply?

Start an application on this website. Progress is saved from the first step. Admissions: admissions@gmuniv.com.

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