Training activity information
Details
Analyse the process used to develop an existing machine learning solution
Type
Developmental training activity (DTA)
Evidence requirements
Evidence the activity has been undertaken by the trainee​.
Reflection on the activity at one or more time points after the event including learning from the activity and/or areas of the trainees practice for development.
An action plan to implement learning and/or to address skills or knowledge gaps identified.
Considerations
- Definition of machine learning
- Implementations of machine learning
- Development of machine learning tools
Reflective practice guidance
The guidance below is provided to support reflection at different time points, providing you with questions to aid you to reflect for this training activity. They are provided for guidance and should not be considered as a mandatory checklist. Trainees should not be expected to provide answers to each of the guidance questions listed.
Before action
- Consider the typical stages involved in developing a machine learning solution.
- Consider what the goal of the machine learning solution is – is it to explain something or to predict something, how will this influence your approach?
- What information do you need to gather about the existing machine learning solution to understand its development process (e.g., data sources, algorithms used, evaluation metrics)?
- What specific insights do you hope to gain about the practical steps and considerations in building a machine learning solution for healthcare?
- How will this activity deepen your understanding of the relationship between development choices and the final performance of a machine learning model?
- What is your current understanding of the machine learning development lifecycle?
- Discuss with your training officer which existing machine learning solution you will analyse and how to access information about its development.
- Review the fundamental steps in machine learning development (e.g., data collection, pre-processing, model selection, training, evaluation).
- Consider potential challenges in obtaining detailed information about the development process and how to approach this.
- How do you feel about investigating and understanding a complex technical development process?
In action
- How are you structuring your analysis of the machine learning solution development process? What aspects (e.g., data preparation, model selection, evaluation) are you focusing on initially?
- What decisions are you making about the key steps and methodologies used in the development process?
- Which parts of the analysis (e.g., understanding algorithms, assessing data quality, interpreting performance metrics) feel more intuitive, and which require more conscious effort?
- How effectively are you identifying the different stages and considerations involved in developing the machine learning solution?
- What challenges are you facing in understanding the rationale behind specific development choices?
- What insights are you gaining about best practices in machine learning development as your analysis proceeds?
- How does this analysis connect with your existing knowledge of machine learning principles and methodologies?
- If certain aspects of the development process are unclear, what alternative ways could you seek to understand them (e.g., reviewing documentation, seeking explanations)?
- Do you need to ask for guidance on interpreting specific technical details of the development process?
- Are you ensuring that your analysis remains focused on the development process itself and within your current technical understanding?
On action
- Summarise the machine learning solution you analysed and the key steps in its development process. What were the different stages involved in developing the solution (e.g., data collection, model training, evaluation)? What methodologies or tools were used in the development process?
- What insights did you gain into the complexities and considerations involved in developing machine learning solutions for healthcare? Were there any unexpected aspects of the development process that you observed? What did you learn from these? How did this activity improve your understanding of the machine learning development lifecycle? How does this knowledge relate to evaluating and potentially contributing to the development of machine learning solutions in your future practice?
- What specific stages of the machine learning development process do you want to understand better? How will you apply your understanding of development processes when evaluating or collaborating on machine learning projects? What actions will you take to deepen your knowledge of machine learning development methodologies and tools? What support or resources might you need to further develop your understanding of machine learning development?
Beyond action
- Have you reviewed your analysis of the machine learning development process? Have you encountered other machine learning solutions and their development processes since this training activity? How do they compare to the one you analysed?
- Has your understanding of machine learning development informed your interactions with data scientists or other professionals involved in AI development? How has this training activity improved your ability to critically evaluate the robustness and reliability of machine learning solutions?
- What transferable skills (e.g., process analysis, critical evaluation of technical processes, understanding of machine learning pipelines) did you develop that will be useful in future engagements with AI development? What specific aspects of machine learning development (e.g., data governance, model validation) might you want to explore further?
Relevant learning outcomes
| # | Outcome |
|---|---|
| # 3 |
Outcome
Plan the implementation of AI and machine learning solutions. |