Backend-agnostic benchmarking suite for evaluating LLM inference systems across local runtimes and hosted APIs, with a focus on latency, throughput, token efficiency, and runtime stability.

End-to-end machine learning projects involve the complete process of developing a machine learning model, starting from data collection and preprocessing to training, evaluation, and deployment. These projects encompass data exploration, feature engineering, model selection, performance evaluation, and integration with production systems