LLM Evaluation on a Custom Dataset with MLflow and Ollama - Financial News Sentiment Analysis

Learn to evaluate and compare four different local Large Language Models using MLflow and Ollama for financial news sentiment analysis tasks. Set up a comprehensive evaluation framework using a custom financial news dataset to determine which model performs best for your specific use case. Begin by exploring the dataset and configuring your notebook environment, then design effective LLM prompts and implement structured output formatting using Pydantic for consistent model responses. Develop evaluation metrics and create an automated evaluation loop to systematically test each model's performance on sentiment classification tasks. Conclude by reviewing and analyzing the experimental results through MLflow's interface to identify the optimal model for financial sentiment analysis applications.