Advanced Occupational Certificate: Machine Learning Developer

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.

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.

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

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 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.