SAQA

All qualifications and part qualifications registered on the National Qualifications Framework are public property. Thus the only payment that can be made for them is for service and reproduction. It is illegal to sell this material for profit. If the material is reproduced or quoted, the South African Qualifications Authority (SAQA) should be acknowledged as the source.

SOUTH AFRICAN QUALIFICATIONS AUTHORITY

REGISTERED QUALIFICATION:

Advanced Occupational Certificate: Machine Learning Developer

SAQA QUAL ID	QUALIFICATION TITLE
125100	Advanced Occupational Certificate: Machine Learning Developer
ORIGINATOR
Development Quality Partner-MICT SETA
PRIMARY OR DELEGATED QUALITY ASSURANCE FUNCTIONARY			NQF SUB-FRAMEWORK
QCTO - Quality Council for Trades and Occupations			OQSF - Occupational Qualifications Sub-framework
QUALIFICATION TYPE	FIELD		SUBFIELD
Advanced Occupational Cert	Field 10 - Physical, Mathematical, Computer and Life Sciences		Information Technology and Computer Sciences
ABET BAND	MINIMUM CREDITS	PRE-2009 NQF LEVEL	NQF LEVEL	QUAL CLASS
Undefined	217	Not Applicable	NQF Level 06	Regular-ELOAC
REGISTRATION STATUS		SAQA DECISION NUMBER	REGISTRATION START DATE	REGISTRATION END DATE
Registered		EXCO 0936/25	2025-11-13	2029-11-13
LAST DATE FOR ENROLMENT		LAST DATE FOR ACHIEVEMENT
2030-11-13		2033-11-13

In all of the tables in this document, both the pre-2009 NQF Level and the NQF Level is shown. In the text (purpose statements, qualification rules, etc), any references to NQF Levels are to the pre-2009 levels unless specifically stated otherwise.

This qualification does not replace any other qualification and is not replaced by any other qualification.

PURPOSE AND RATIONALE OF THE QUALIFICATION

Purpose:

The purpose of this qualification is to prepare a learner to function as a Machine Learning Specialist.

A Machine Learning Specialist applies expertise in machine learning to design, build, test and deploy machine learning systems to solve specific problems or automate tasks. It involves multiple stages, from identifying the problem and collecting data to creating models and integrating them into practical applications. Machine Learning Specialists have extensive knowledge and expertise in machine learning principles, data science, statistical, predictive modelling and AI algorithms and are proficient in applicable programming languages.

The qualification equips learners with critical technical skills in building algorithms, processing data, and developing AI solutions, which are highly sought after in various industries. Learners enhance problem-solving and analytical abilities, thus opening doors to lucrative career opportunities. Candidates would stay at the forefront of innovation, enabling them to create impactful solutions in a rapidly evolving and data-driven world. They create efficient, scalable, cost-effective machine learning solutions that generate actionable insights, automate tasks and drive business decisions.

A qualified learner will be able to:

Design and deploy machine learning models.

Optimise and maintain machine learning solutions.

Implement ethical artificial intelligence and uphold responsible machine learning practices.

Typical Graduate attributes:

Analytical.

Patient.

Meticulous.

Communication.

Collaborative.

Teamwork.

Critical thinking.

Problem-solving.

Rationale:
As industries increasingly adopt machine learning (ML) to drive innovation, there is a growing demand for skilled Machine Learning Specialists who can design, implement and maintain these systems effectively. A formal qualification in machine learning serves as a bridge to meet this demand by equipping individuals with the necessary knowledge and expertise. A Machine Learning Specialist qualification is a necessity in today's data-driven world. It ensures that professionals are equipped to harness the full potential of machine learning to create innovative, ethical, and impactful solutions while driving economic growth and societal progress. A similar qualification registered on the NQF is Occupational Certificate: Artificial Intelligence Software Developer, NQF Level 05.

This qualification ensures a standardised skill set, fostering innovation and reliability in AI solutions. It enhances trust in AI-driven systems, promotes ethical practices, and reduces risks of poorly designed models. It encourages professional growth and collaboration, enabling industries to adopt AI confidently, ultimately driving societal progress, efficiency, and technological advancements.

The Advanced Occupational Certificate: Machine Learning Developer equips professionals with cutting-edge skills, enabling the sector to innovate faster and address complex challenges. It fosters expertise in designing robust, scalable, and ethical AI systems, boosting efficiency and reliability. It improves technical proficiency which attracts investments, ensures better collaboration across industries, and drives competitive advantage, ultimately positioning the sector as a cornerstone of transformative technological and economic progress.

This qualification equips individuals with advanced skills to design intelligent systems, driving innovation across industries. By optimising processes, improving decision-making, and enabling automation, these professionals boost productivity and reduce costs. It fosters the development of data-driven solutions, creating new business opportunities and enhancing competitiveness. This, in turn, stimulates job creation, attracts investment, and accelerates economic growth, positioning economies in the forefront of the global tech landscape.

Typical learners include professionals who are currently functioning as Data Scientists and Analysts, Software Engineers and Developers, Artificial Intelligence Developers who want to advance their careers and Machine Learning Experts functioning without formal recognition.

This qualification was developed in collaboration with relevant stakeholders:

Skills Development Providers.

Employers.

Practitioners.

Post School Education and Training Institutions.

Typical occupations or professions in which the qualifying learner will operate include:

Machine Learning Engineer.

Machine Learning Scientist.

Predictive Analytics Developer.

Deep Learning Specialist.

Algorithm Engineer.

Data Engineer.

AI Solutions Architect.

LEARNING ASSUMED TO BE IN PLACE AND RECOGNITION OF PRIOR LEARNING

Recognition of Prior Learning (RPL):
RPL for access:
Learners may use the RPL process to gain access to training opportunities for a qualification if they do not meet the formal, minimum entry requirements for admission. RPL assessment provides an alternative access route into a qualification.

Such an RPL assessment may be developed, moderated and conducted by the accredited Skills Development Provider which offers that specific qualification. Such an assessment must ensure that the learner is able to display the equivalent level of competencies required for access, based on the NQF level descriptors.

RPL for credits:
For exemption from modules through RPL, learners who have gained the stipulated competencies of the modules of qualification through any means of formal, informal or nonformal learning and/or work experience, may be awarded credits towards relevant modules, and gaps identified for training, which is then concluded.

RPL for Access to the External Integrated Summative Assessment (EISA):
Learners who have gained the stipulated competencies of the modules of a qualification through any means of formal, informal or non-formal learning and/or work experience, may be awarded credits towards relevant modules, and gaps identified for training, which is then concluded. A valid Statement of Results is required for admission to the EISA in which confirmation of achievement is provided that all internal assessment criteria for all modules in the related curriculum document have been achieved.

Upon successful completion of the EISA, RPL learners will be issued with the QCTO certificate for the qualification. Quality Partners are responsible for ensuring the RPL mechanism and process for qualifications and part-qualification is approved by the QCTO.

Entry Requirements:
An NQF Level 05 qualification in Data Science and Analysis, Software Engineering and Development, Artificial Intelligence
Or
ICT related qualification at NQF Level 05.

RECOGNISE PREVIOUS LEARNING?

QUALIFICATION RULES

This qualification is made up of compulsory Knowledge, Practical Skill and Work Experience Modules:

Knowledge Modules:

251201-004-00-KM-01, Foundations of Machine Learning, Level 6, 2 Credits.

251201-004-00-KM-02, Machine Learning Fundamentals, Level 6, 4 Credits.

251201-004-00-KM-03, Mathematics for Machine Learning, Level 6, 8 Credits.

251201-004-00-KM-04, Advanced Machine Learning, Level 6,12 Credits.

251201-004-00-KM-05, Data Processing and Analysis, Level 6, 4 Credits.

251201-004-00-KM-06, Supervised Learning, Level 6, 8 Credits.

251201-004-00-KM-07, Unsupervised Learning, Level 6, 8 Credits.

251201-004-00-KM-08, Reinforcement Learning (RL), Level 6,8 Credits.

251201-004-00-KM-09, Deep Learning, Level 6, 8 Credits.

251201-004-00-KM-10, Business Foundations and Processes for Machine Learning Specialists, Level 6, 2 Credits.

251201-004-00-KM-11, Ethics and Governance in Machine Learning, 6, 2 Credits.

Total number of credits for Knowledge Modules: 66

Practical Skill Modules:

251201-004-00-PM-01, Apply Machine Learning Workflows, Level 6, 20 Credits.

251201-004-00-PM-02, Select, Train, Evaluate and Optimise Machine Learning Models, Level 6, 28 Credits.

251201-004-00-PM-03, Comply with Ethical and Scalability AI for Large Datasets, Level 6, 4 Credits.

251201-004-00-PM-04, Deploy the Trained Model, Monitor Performance and Report, Level 6, 28 Credits.

251201-004-00-PM-05, Complete a Capstone Project, Level 6, 16 Credits.
Total number of credits for Practical Skill Modules: 96

Work Experience Modules:

251201-004-00-WM-01, Problem Definition and Analysis, Level 6, 6 Credits.

251201-004-00-WM-02, Machine Learning Model Design and Optimisation, Level 6, 17 Credits.

251201-004-00-WM-03, Real-Time Machine Learning Model and Solution Deployment and Maintenance, Level 6, 24 Credits.

251201-004-00-WM-04, Insight Presentation, Level 6, 8 Credits.

Total number of credits for Work Experience Modules: 55

EXIT LEVEL OUTCOMES

1. Apply machine learning concepts and techniques.
2. Conduct data preprocessing and feature engineering.
3. Develop machine learning models.
4. Deploy machine learning models at scale.
5. Develop deep learning and neural networks.
6. Maintain machine learning pipelines.
7. Apply ethical AI and responsible machine learning principles.

ASSOCIATED ASSESSMENT CRITERIA

Associated Assessment Criteria for Exit Level Outcome 1:
ELO 1: Apply machine learning concepts and techniques.

Explain and apply supervised, unsupervised, and reinforcement learning techniques.

Explain and apply key Machine Learning concepts (such as bias-variance trade-off, overfitting, underfitting, and generalization).

Compare various Machine Learning algorithms (including regression, decision trees, neural networks, and ensemble methods) in terms of performance and application.

Apply probability, statistics, and linear algebra in the formulation and evaluation of Machine Learning models.

Associated Assessment Criteria for Exit Level Outcome 2:
ELO 2: Conduct data preprocessing and feature engineering

Collect, clean, and transform data to ensure suitability for ML applications.

Perform exploratory data analysis (EDA) to identify patterns, trends, outliers, and data imbalances using visualization and statistical techniques.

Apply feature selection, extraction, and transformation techniques.

Handle missing data, categorical variables, and outliers according to best practices.

Associated Assessment Criteria for Exit Level Outcome 3:
ELO 3: Develop machine learning models

Implement Machine Learning models, trained and fine-tuned using appropriate frameworks such as TensorFlow, PyTorch, and Scikit-Learn.

Evaluate and optimise Model performance using appropriate metrics such as accuracy, precision, recall, F1-score, RMSE and ROC-AUC.

Conduct hyperparameter tuning using techniques such as grid search, random search, and Bayesian optimisation to improve model performance.

Apply cross-validation and resampling techniques to assess model robustness.

Interpret, visualise, and report model performance results with clear justification for model choices.

Associated Assessment Criteria for Exit Level Outcome 4:
ELO 4: Deploy machine learning models at scale

Deploy Machine Learning models using cloud-based platforms (e.g., AWS, GCP, Azure) and containerisation tools (Docker, Kubernetes).

Implement Model versioning, monitoring, and logging to track performance and ensure stability.

Integrate Machine Learning models into production pipelines using CI/CD workflows and MLOps best practices.

Assess and optimise the scalability, efficiency, and reliability of deployed Machine Learning solutions.

Associated Assessment Criteria for Exit Level Outcome 5:
ELO 5: Develop deep learning and neural networks

Explain the architecture, advantages, and applications of CNNs, RNNs, GANs, and transformers with examples.

Implement deep learning models using TensorFlow, PyTorch, or Keras for various tasks such as image recognition, natural language processing, and time series forecasting.

Apply transfer learning and fine-tuning of pre-trained models (e.g., BERT, ResNet, GPT) for task-specific applications.

Optimise deep learning models using dropout, batch normalisation, weight initialisation, regularisation, and learning rate scheduling.

Improve computational efficiency using hardware accelerators (GPUs, TPUs) and model quantisation techniques.

Associated Assessment Criteria for Exit Level Outcome 6:
ELO 6: Maintain machine learning pipelines

Monitor ML pipelines continuously for data drift, model drift, and performance degradation.

Implement automated alerts and model retraining strategies (active learning, periodic retraining) to maintain accuracy over time.

Document data and pipeline dependencies to prevent breakages and ensure maintainability.

Performance and business impact are regularly evaluated, with necessary updates and improvements applied.

Associated Assessment Criteria for Exit Level Outcome 7:
ELO 7: Apply ethical AI and responsible machine learning principles

Identify and mitigate Bias in Machine Learning models and datasets.

Incorporate fairness, accountability, and transparency principles in AI applications.

Comply with legal and ethical AI guidelines (e.g., GDPR, AI Act).

Assess and minimize risks associated with AI deployment.

Integrated Assessment:
Integrated Formative Assessments:

Formative assessments are conducted throughout the training of learners. A range of formal, non-formal, and informal ongoing assessment activities are used to focus on teaching and learning outcomes to improve learner attainment.

Formative assessments are conducted continuously by the facilitator to feed into further learning, to identify strengths and weakness, and to ensure the learner's ability to apply knowledge, skills and workplace experience gained.

Formative Assessments are conducted by the accredited Skills Development Provider (SDP), and a variety of ongoing assessment methods may be used, for example, quizzes, assignments, tests, scenarios, role play, and interviews. Continuous feedback must be provided.

Integrated Summative Assessments:

Integrated Assessment involves all the different types of assessment tasks required for a particular qualification, such as written assessment of theory and practical demonstration of competence. To achieve this, the Internal Assessment Criteria (IAC) for all modules as found in the QCTO curriculum document must be followed.

An accredited SDP should implement a well-designed, formal, relevant, final internal Summative Assessment strategy for all modules to prepare learners for the EISA. These assessments evaluate learning achievements relating to the achievement of each module of the relevant components of the qualification.

Internal Summative Assessments are developed, moderated and conducted by the SDP at the end of each module or after integration of relevant modules, e.g. applied knowledge tests, workplace tasks, practical demonstrations, simulated tasks/demonstrations, projects, case studies, etc.

The results of these final formal summative assessments must be recorded. These results, which include the Statement of Work Experience results, where applicable, contribute to the Statement of Results (SoR) that is a requirement for admission to the EISA. An SoR, using the template provided by the Quality Partner, is issued by the accredited SDP for qualifications and part-qualifications. The SDP must produce a valid Statement of Results for each learner, indicating the final result and the date on which the competence in each module, of each component, was achieved. Learners are required to produce this SoR, together with their ID document or alternative ID document, at the point of the EISA.

External Integrated Summative Assessment (EISA) a national assessment.
The Quality Partner is responsible for the management, conduct and implementation of the External Integrated Summative Assessment (EISA), in accordance with QCTO set standards. Competence in the EISA is a requirement for certificating a learner.

For entrance into the EISA, the learner requires a valid Statement of Results issued by the accredited institution indicating:

The attainment of all modules for the Knowledge, Practical, and Work Experience modules.
Or
The attainment of all modules for the Knowledge and Application Components.

INTERNATIONAL COMPARABILITY

This qualification was compared to the following international qualifications:

Country: United Kingdom
Institution: Severn Business College (SBC).
Qualification title: Graduate Diploma in Artificial Intelligence.

The Level 6 Graduate Diploma in Artificial Intelligence, offered by Severn Business College (SBC), is a 120-credit qualification and is assessed through practical assignments rather than exams. It provides a comprehensive foundation in AI, covering machine learning, natural language processing, data analytics, and intelligent systems design.

This practical, assignment-based diploma is delivered via distance learning, allowing learners the flexibility to complete it in 12 months, with rolling intakes. Qualifying learners can advance to a Level 7 qualification or an MBA top-up. It provides for an in-depth understanding of AI principles, techniques, and applications.

The curriculum consists of six key modules:

Introduction to Artificial Intelligence, 20 credits - Covers fundamental AI concepts, history, and ethical considerations.

Machine Learning Fundamentals, 20 credits - Explores algorithms, supervised and unsupervised learning, and deep learning basics.

Natural Language Processing, 20 credits - Focuses on text processing, speech recognition, and AI-driven language models.

Data Analytics and AI, 20 credits - Examines data preprocessing, statistical analysis, and predictive modelling.

Intelligent Systems Design, 20 credits - Teaches AI integration in robotics, automation, and smart technologies.

Advanced AI Techniques, 20 credits - Covers reinforcement learning, AI in IoT, and ethical AI solutions.
Entry requirements are specified as the minimum age of 18, a Level 5 qualification (or equivalent) or a mature applicant with qualification and work experience.

Similarities:

Both qualifications have comparable level of complexity, cover overlapping content areas, including machine learning, algorithms, supervised and unsupervised learning, deep learning, and data analytics, data preprocessing, statistical analysis, predictive modelling, and ethical considerations in AI.

Each qualification requires a Level 5 qualification for entry and serves as a pathway to a Level 7 qualification.

Differences:

The SBC Graduate Diploma in Artificial Intelligence covers topics such as intelligent systems design and the integration of AI into robotics, automation, and smart technologies.

In contrast, the Advanced Occupational Certificate: Machine Learning Specialist only briefly covers microprocessors, IoT, and embedded systems. The SBC qualification provides a broader foundation in artificial intelligence, while the South African qualification is more specialised in machine learning and assumes prior knowledge of AI.

The Advanced Occupational Certificate: Machine Learning Developer specifies work experience and concludes with a final national examination, whereas the Graduate Diploma in Artificial Intelligence does not have a final examination.

Country: Namibia
Institution: National Institute of Technology (NIT).
Qualification title: Diploma in Artificial Intelligence and Machine Learning

The Diploma in Artificial Intelligence and Machine Learning (Level 6) is offered by the National Institute of Technology (NIT). This comprehensive qualification is designed to equip learners with the essential skills and knowledge needed for careers in AI and ML. The diploma provides a strong foundation in key areas such as data science, deep learning, neural networks and algorithm development, ensuring that qualifying learners are well-prepared to tackle real-world AI applications. It is ideal for individuals pursuing careers in AI, ML, and data science, with opportunities in data analytics.

Upon completion, qualifying learners will be qualified to work as AI specialists, machine learning engineers, or data scientists. Additionally, this qualification serves as a pathway to the Bachelor of Technology in Artificial Intelligence and Machine Learning.

To be eligible for this qualification, applicants must meet NIT's General Admission Requirements. Specifically, candidates must have successfully completed at least 80% or all units of the Diploma in Artificial Intelligence and Machine Learning (Level 5). This diploma offers a rigorous and industry-aligned curriculum, preparing learners to excel in AI and ML- driven careers while providing a strong academic foundation for further studies. The Diploma in Artificial Intelligence and Machine Learning consists of twelve units and is designed for flexible learning. It can be completed in one year through various study modes, including distance learning, online learning, virtual campus, part-time, full-time, or blended learning.

The key topics covered in the qualification include:

Core AI and ML Concepts - covering machine learning models, supervised and unsupervised learning, and deep learning techniques.

Programming and Data Science - focusing on Python, data preprocessing, and feature engineering.

AI Applications - including computer vision, natural language processing (NLP), and AI-driven decision-making.

Specific units are (inter alia):

Machine Learning and Neural Networks.

Intelligent Signal Processing.

Artificial Intelligence Interaction Design.

Cloud Computing.

Intelligent Robotics.

Computational Mathematics.

Ethics, Corporate Governance, and Business Law.

Specialized Industry-Related Work Experience.

Similarities:

Both qualifications have a comparable level of complexity, cover similar topics, including data science, deep learning, neural networks, and algorithm development and mathematics.

Both qualifications require a Level 5 qualification for entry into a qualification.

They both prepare learners for the world of work and include units related to industry related work experience.

Differences:

The Diploma in Artificial Intelligence and Machine Learning includes an element of Artificial Intelligence whereas the Advanced Occupational Certificate: Machine Learning Specialist focuses on Machine Learning and assumes prior knowledge of AI.

The Diploma in Artificial Intelligence and Machine Learning includes units on cloud computing and intelligent robotics, which are not part of the Advanced Occupational Certificate: Machine Learning Specialist curriculum.

Conclusion:
The Advanced Occupational Certificate: Machine Learning Specialist compares favourably with the international qualifications in terms of level of complexity, content, module components and entry requirements.

ARTICULATION OPTIONS

This qualification provides opportunities for horizontal, vertical and diagonal articulation options.

Horizontal Articulation:

Occupational Certificate: Software Engineer, NQF Level 6.
Note: This qualification will reach its registration end date in December 2025. The last date of enrolment is December 2026.

Advanced Certificate in Information Technology Governance, NQF Level 6.

Vertical Articulation:

There are no vertical articulation possibilities within the subframework since the contents of this qualification are still new. Articulation will be updated as soon as a related qualification gets registered.

Diagonal Articulation:

Bachelor of Commerce in Information Technology Management, NQF Level 7.

MODERATION OPTIONS

N/A.

CRITERIA FOR THE REGISTRATION OF ASSESSORS

N/A.

NOTES

Additional Legal or Physical Entry Requirements:

None

Criteria for the accreditation of providers:

Accreditation requirements, against which Skills Development Providers (SDP) and Assessment Centres, will be accredited, is found in the Curriculum Document, as listed below.

Curriculum code: 251201-004-00-00

Encompassed Trade:
This qualification encompasses the following trades as recorded on the NLRD

None

Assessment Quality Partner (AQP):

Media, Information and Communication Technologies SETA.

LEARNING PROGRAMMES RECORDED AGAINST THIS QUALIFICATION:

NONE

PROVIDERS CURRENTLY ACCREDITED TO OFFER THIS QUALIFICATION:

This information shows the current accreditations (i.e. those not past their accreditation end dates), and is the most complete record available to SAQA as of today. Some Primary or Delegated Quality Assurance Functionaries have a lag in their recording systems for provider accreditation, in turn leading to a lag in notifying SAQA of all the providers that they have accredited to offer qualifications and unit standards, as well as any extensions to accreditation end dates. The relevant Primary or Delegated Quality Assurance Functionary should be notified if a record appears to be missing from here.

NONE