Store immutable AI evaluation records with EvalHub and OCI

This post explores how EvalHub's integration with OCI can enhance the immutability and verifiability of AI evaluation records, addressing issues in AI reproducibility by ensuring that evaluation results are immutable and can be independently verified. It details the configuration of OCI exports within EvalHub and how it works seamlessly with MLflow for tracking experiments. The content is part of a series on building AI evaluation infrastructure and includes technical configurations, compliance implications, and the advantages of utilizing OCI persistence.