Arize AI is a machine learning observability and large language model (LLM) evaluation platform. It provides users with real-time monitoring and analytics to identify, diagnose, and fix issues in AI models. The platform helps ensure that AI pipelines are running smoothly by surfacing potential failures, data drifts, and performance issues, thereby facilitating faster troubleshooting and more reliable AI outcomes. Arize aims to eliminate the black box concern in AI, making models more transparent and understandable.

Mean-Field MARL provides a robust Python framework for implementing and evaluating mean-field multi-agent reinforcement learning algorithms. It approximates large-scale agent interactions by modeling the average effect of neighboring agents via mean-field Q-learning. The library includes environment wrappers, agent policy modules, training loops, and evaluation metrics, enabling scalable training across hundreds of agents. Built on PyTorch for GPU acceleration, it supports customizable environments like Particle World and Gridworld. Modular design allows easy extension with new algorithms, while built-in logging and Matplotlib-based visualization tools track rewards, loss curves, and mean-field distributions. Example scripts and documentation guide users through setup, experiment configuration, and result analysis, making it ideal for both research and prototyping of large-scale multi-agent systems.