Issue #11 of The Comet Newsletter introduces Comet Artifacts, a new tool designed to aid machine learning (ML) teams in logging, versioning, and accessing data throughout their experimentation workflows. This tool allows for the management of data produced in ML experiments by creating versioned objects known as Artifacts, which provide an immutable snapshot of files and assets in a structured format. This innovation supports the construction of multi-stage pipelines and Directed Acyclic Graphs (DAGs), ensuring centralized and versioned data access. The newsletter also highlights industry developments, such as OpenAI Codex, which is built on GPT-3 architecture to assist in AI-driven software development, and explores topics like ML model fairness, transparency, and decision-making ethics. Additionally, it features insights from a detailed resource by Jay Alammar focusing on Transformer architectures, offering various perspectives on this prevalent deep learning model.