📘 Lesson Summary:
This lesson covers Workplace Experience Task WE06. Learners develop parts of an AI solution under workplace supervision, using technical designs, datasets, and organisational development tools.
Lesson 1: Building AI Solution Components in the Workplace (WE06)
This lesson focuses on the hands-on development of AI solution components within a real or simulated workplace environment. Based on the technical design and prepared datasets from previous tasks, learners contribute to building the AI system in alignment with organisational standards.
⭐ WE06: Develop AI Solution Components
Learners work under supervision to:
- Implement parts of the AI solution using approved tools (Python, APIs, libraries, frameworks)
- Apply the technical design prepared in WE04
- Use the cleaned and analysed dataset from WE05
- Build components such as:
- Data pre-processing functions
- Machine learning models
- Feature extraction processes
- Evaluation functions
- Prediction scripts
- Troubleshoot errors during development
- Test their components to ensure correct functionality
- Document changes, updates, and challenges encountered
These tasks represent the real work that junior AI developers and data practitioners perform when contributing to production systems.
⭐ Workplace Expectations
During this task, learners are expected to:
- Follow organisational coding standards
- Use appropriate version control processes (e.g., Git) if applicable
- Communicate challenges clearly with supervisors
- Produce clean, well-structured code
- Maintain workplace documentation
- Ensure their components fit into the larger AI solution architecture
⭐ Tools Typically Used in WE06
Examples include:
- Python (NumPy, Pandas, Scikit-learn, TensorFlow, etc.)
- Jupyter Notebook / VS Code
- APIs and integration tools
- Workplace databases
- Logging and performance-checking tools