The way actual users experience your system is essential to assessing the true impact of its predictions, recommendations, and decisions.
The use of several metrics rather than a single one will help you to understand tradeoffs between different kinds of errors and experiences.
ML models will reflect the data they are trained on, so analyze your raw data carefully to ensure you understand it. In cases where this is not possible, e.g., with sensitive raw data, understand your input data as much as possible while respecting privacy; for example by computing aggregate, anonymized summaries.
Learn from software engineering best test practices and quality engineering to make sure the AI system is working as intended and can be trusted.
Continued monitoring will ensure your model takes real-world performance and user feedback (e.g., happiness tracking surveys, HEART framework) into account.
Do you want to compare your organisation with peers on Data Science Success Ladder?
Our Philosophy - The Data Science Success Ladder
Data Maturity Assessment
We will be asking 10 questions to understand your needs better and also provide you the industry insights by comparing your stage with other clients we work with. It will take 3 minutes to complete.