Why AI/Data Science Projects Fail – How to Avoid Project Pitfalls
Recent data shows that 87% of AI/Big Data projects don’t make it into production (VB Staff, 2019). This talk will discuss five common pitfalls and how to avoid them: scope sizing, scope creep, explainability, model complexity, and solving the correct problem. Metrics that can be applied to determine business value will be discussed and equations to calculate these metrics will be offered.Five phases of a machine learning project will be discussed: Define project objectives; acquire and explore data; model data; interpret and communicate; and implement, document, and maintain (DataRobot, 2018). This talk will present tools you can use throughout your next project to avoid pitfalls and ensure your project can succeed despite the odds.
Data Scientist at Intel Corporation
Joyce Weiner is a data scientist and Lean expert at Intel Corporation. She has a BS in physics from Rensselaer Polytechnic Institute, and an MS in optical sciences from the University of Arizona.