This subtopic covers the capacity of learning engineering team members to scale instrumentation architectures.
- Scalability of Data Systems: Learning engineering teams must consider how scalable their data instrumentation systems are, especially when planning for widespread adoption or scaling up learning solutions. Data systems need to support increasing data volumes without degrading performance, ensuring that data collection remains efficient and manageable.
- Balancing Data Richness and Feasibility: Rich datasets are invaluable, but the feasibility of collecting extensive data must be evaluated. High data collection costs, system performance concerns, and learner burden are factors that influence how much and what type of data should be collected. Learning engineers must weigh the benefits of rich data against potential downsides to feasibility and learner experience.