SE4ML - Software Engineering for Machine Learning - Nadia Nahar

We talked about:

• Nadia’s background


• Academic research in software engineering


• Design patterns


• Software engineering for ML systems


• Problems that people in industry have with software engineering and ML


• Communication issues and setting requirements


• Artifact research in open source products


• Product vs model


• Nadia’s open source product dataset


• Failure points in machine learning projects


• Finding solutions to issues using Nadia’s dataset and experience


• The problem of siloing data scientists and other structure issues


• The importance of documentation and checklists


• Responsible AI …

academic communication data data scientists dataset design documentation engineering engineers experience failure importance industry machine machine learning machine learning projects open source patterns people product products projects requirements research responsible ai scientists software software engineering software engineers solutions systems work