Improving Machine Learning Development Reliability

Improving Machine Learning Development Reliability

Explore the unique challenges and differences between Machine Learning Development Lifecycle and Software Development Lifecycle in this 42-minute conference talk from SREcon22 APAC. Delve into the complexities of ML reliability and scalability as Brian Hansen and Yan Yan from Meta discuss the need for new approaches to building, monitoring, and alerting on ML artifacts. Gain insights into Meta's strategies and understand the importance of community involvement in evolving the development and productization of machine learning. Learn why traditional software development methods may not suffice for ML projects and discover potential solutions to improve machine learning development reliability as the field rapidly expands across industries.