The global skills and competency framework for the digital world

Skills focused on building the AI/ML models

AI literacy, Using AI tools to automate, assist, augment, Skills focused operationalising AI/ML applications, Skills focused on building the AI/ML models

Skills focused on building the AI/ML models

Scope:

  • This particular skill set is relevant to a specialised group of professionals, such as data scientists, machine learning engineers, and AI researchers.
  • Requires advanced education, training, and access to specialised resources.

Impact:

  • Directly contributes to the organisation's AI/ML capabilities and intellectual property
  • Drives innovation and differentiation through the development of organisation-specific AI/ML models
  • Enables the organisation to solve complex business problems, generate valuable insights, develop new products and services

See also SFIA 9 updates for Data and analytics skills 

SFIA 9 skills focused on building the AI/ML models

SFIA skill - name Code SFIA skill - concise description Example applications of the skill
Data science DATS Applying mathematics, statistics, data mining and predictive modelling techniques to gain insights, predict behaviours and generate value from data. Using predictive modelling techniques to analyse customer data and forecast purchasing behaviour. This helps the marketing team target their campaigns more effectively, increasing sales and customer retention.
Developing a customer segmentation model using clustering algorithms to identify distinct customer groups and tailor marketing strategies accordingly.
Formal research RSCH Systematically creating new knowledge by data gathering, innovation, experimentation, evaluation and dissemination.

Conducting research on novel deep learning architectures to improve the accuracy and efficiency of image recognition models.
Emerging technology monitoring EMRG Identifying and assessing new and emerging technologies, products, services, methods and techniques. A technology specialist monitors the latest advancements in AI, identifying a new deep learning framework that can be used to enhance image recognition capabilities in the company's products. This early adoption gives the company a competitive edge.
Evaluating the potential of graph neural networks for enhancing fraud detection in financial transactions.
Machine learning MLNG Developing systems that learn through experience and by the use of data. A machine learning engineer develops a recommendation engine for an e-commerce platform. By analysing user data and using collaborative filtering and matrix factorization techniques, the engine provides personalised product recommendations, increasing user engagement and sales.
Numerical analysis NUAN Creating, analysing, implementing, testing and improving algorithms for numerically solving mathematical problems. An engineer creates and tests algorithms to optimise supply chain logistics, reducing costs and improving delivery times. This involves solving complex mathematical problems related to inventory management and transportation.
Optimising the hyperparameters of a gradient boosting model using grid search and cross-validation techniques to improve its predictive performance.
High-performance computing HPCC Using advanced computer systems and special programming techniques to solve complex computational problems. Implementing a distributed training pipeline for a large-scale natural language processing model using Apache Spark and GPU clusters to reduce training time and handle massive datasets.
A computational scientist uses cloud-based computing resources to run complex AI models for natural language processing. By leveraging scalable computing, the scientist can process large datasets quickly, enabling real-time language translation services.