The Comet Newsletter's issue #17 covers a spectrum of topics in machine learning and AI, including emerging skepticism about the correlation between the size of language models and their performance, as exemplified by the FLAN architecture's ability to generalize across tasks. The newsletter features a comprehensive guide on addressing concept drift in production machine learning models, emphasizing the necessity for ongoing adaptation to dynamic real-world conditions and highlighting practical solutions and ethical considerations. It also provides insights into graph neural networks through a two-part series by Google researchers, detailing their structure, functionality, and the implications of graph convolutions. Additionally, the newsletter includes guidance from Stanford instructor Chip Huyen on deploying ML models on the edge, covering aspects of model compatibility, performance, and optimization methods.