Skill description
Developing systems that learn from data and experience, improving performance, accuracy and adaptability in dynamic environments.
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.
Level 2Assist
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.
Level 3Apply
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.
Level 4Enable
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.
Level 5Ensure, advise
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.
Level 6Initiate, influence
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.
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