Machine learning MLNG
Developing systems that learn from data and experience, improving performance, accuracy and adaptability in dynamic environments.
Revision notes
Updates for SFIA 9
- Theme(s) influencing the updates for this skill: Continued refinement for AI/ML Ops skills, Making SFIA easier to consume (enhance readability/guidance/descriptions), Making SFIA easier to consume (updates to skill name/skill description).
- Readability improvements have been made to levels 2, 3, 4, 5, and 6.
- You can move to SFIA 9 when you are ready - SFIA 8 skill descriptions will still be available to use.
- Previous SFIA assessments or skills mapping are not impacted by this change.
Guidance notes
Activities may include, but are not limited to:
- assessing the suitability of machine learning for business problems
- selecting and applying appropriate machine learning techniques, algorithms and tools to solve business problems
- preparing data for machine learning, including cleansing, transformation and feature engineering
- designing, training, optimising and retraining models using supervised, unsupervised or reinforcement learning
- managing MLOps for model deployment, monitoring and lifecycle management
- evaluating models for performance, robustness, fairness and bias, and selecting metrics to assess outcomes
- diagnosing and resolving issues before and after deployment
- anticipating organisational implications, including ethics, bias, privacy, sustainability and data protection
- establishing traceability for outcomes produced by machine learning systems
- implementing continuous learning mechanisms to ensure models adapt to new data and changing environments, including real-time adaptation to new inputs and evolving conditions.
Understanding the responsibility levels of this skill
Where lower levels are not defined...
- Specific tasks and responsibilities are not defined because the skill requires a higher level of autonomy, influence, and complexity in decision-making than is typically expected at these levels. You can use the essence statements to understand the generic responsibilities associated with these levels.
Where higher levels are not defined...
- Responsibilities and accountabilities are not defined because these higher levels involve strategic leadership and broader organisational influence that goes beyond the scope of this specific skill. See the essence statements.
Developing skills and demonstrating responsibilities related to this skill
The defined levels show the incremental progression in skills and responsibilities.
Where lower levels are not defined...
You can develop your knowledge and support others who do have responsibility in this area by:
- Learning key concepts and principles related to this skill and its impact on your role
- Performing related skills (see the related SFIA skills)
- Supporting others who are performing higher level tasks and activities
Where higher levels are not defined...
- You can progress by developing related skills which are better suited to higher levels of organisational leadership.
Click to learn why SFIA skills are not defined at all 7 levels.
Show/hide extra descriptions and levels.
Levels of responsibility for this skill
| 2 | 3 | 4 | 5 | 6 |
Level 1
Machine learning: Level 2
Assists in data preparation, model training and evaluation tasks under routine supervision.
Uses standard machine learning frameworks and tools to develop basic models for well-defined problems.
Documents results and contributes to maintaining machine learning solutions.
Machine learning: Level 3
Applies established machine learning techniques and algorithms to solve business problems.
Selects and prepares data for model training and evaluation.
Trains, optimises and validates machine learning models using standard tools and frameworks.
Deploys models into production and monitors their performance. Communicates results and limitations to stakeholders.
Machine learning: Level 4
Assesses the suitability of machine learning and designs and develops solutions for a range of business problems.
Selects and applies appropriate techniques and algorithms based on data characteristics and business requirements. Provides guidance to others.
Engineers features and optimises model performance. Implements algorithms and contributes to development, evaluation, monitoring and deployment. Applies industry-specific rules and guidelines, anticipating risks and implications.
Collaborates with cross-functional teams to integrate machine learning models into production systems. Conducts in-depth performance analysis and troubleshoots issues.
Machine learning: Level 5
Leads the development and implementation of machine learning solutions for complex, high-impact business problems.
Architects end-to-end machine learning pipelines and systems, incorporating MLOps practices. Evaluates and selects tools, frameworks and infrastructure for machine learning projects.
Establishes practices and standards for machine learning development and operations. Provides expert advice and guidance on machine learning techniques and applications.
Collaborates with stakeholders to align machine learning initiatives with organisational goals.
Machine learning: Level 6
Sets the strategic direction and roadmap for machine learning adoption and innovation within the organisation.
Establishes governance frameworks and recommended protocols for responsible, ethical and sustainable development and use of machine learning.
Leads the development of organisational capabilities, policies, standards and guidelines in machine learning.
Collaborates with senior stakeholders to identify high-impact opportunities for machine learning and drives their implementation. Follows research and industry trends and integrates them into organisational practices.