o Distribution/dataset shift can occur where a machine learning model is deployed on a data distribution that is different to what it was trained on New risks identified at closure (linked to potential future work):  Errors made when importing large volumes of historic data could re-incur processing costs  There is a risk that recommendations from the Evaluation are not considered as part of further development work 4.4.8 Feasibility summary There is still significant development work to do to refine and iterate the products. The evidence for data retrieval and ecomaps presented at Section 4.4.2 (Evidence – Time Spent on Data Retrieval Task) and Section 4.4.3 (Evidence – Children’s Networks) shows significant improvements in terms of data retrieval tasks completed faster and expanding children’s networks, however this was based on a very small sample. This was unavoidable as the scale and duration of the pilot groups were tailored to something proportionate and appropriate to the stage of the development of the tool, and the timescales of the project. The benefit of gathering feedback from users at an early stage in development is that this is aligned with co- production/user-centric design principles. Feedback from users at pilot workshops was very positive, and an evaluation of this is included at Section 4.3 (Evaluation of Expected Benefits). There were no significant barriers or issues presented during the risk management process and mitigations developed were appropriate, thorough and effective. This evaluation report and supporting documents including practice guidance, data factory documentation and the “packaged” versions of the data factory infrastructure and SQL Database could enable other organisations to set something similar up with minimum effort. 4.5 Ac