- 87% of AI / Big Data projects don’t make it into production, meaning that most projects are never deployed
- Five common pitfalls: scope sizing, scope creep, explainability, model complexity, and solving the correct problem
- Tools and methods to apply during the five phases of a data science project to avoid the common pitfalls
- Stories from actual experience in building and deploying AI and machine learning models will be shared
Joyce Weiner
Principal Engineer, AI Software Architecture at Intel Corporation
As a Lean expert and Data Scientist, she focuses on using data to drive change and improve efficiency. Joyce is currently working on projects to use AI to assist system designers, and to unlock insights from text fields in customer sighting data. Her book, “Why AI/Data Science Projects Fail: How to Avoid Project Pitfalls” was published in 2021. Joyce has a BS in Physics from Rensselaer Polytechnic Institute, and an MS in Optical Sciences from the University of Arizona. She is married and in her free time enjoys drawing, calligraphy, and reading.