Experiment tracking - Metadata store

[MacerPacer]

Hey,

i got a question regarding the MLOps-Principles. Is the metadatastore one component of experiment tracking or are those different aspects of MLOps. What i thought, experiment tracking tracks the metadata for the experiments in creating a training pipeline and the metadata store tracks the information about the pipeline runs in production. Or is everything of that included in experiment tracking?

Can somebody help me with that? Thanks in advance

[Joevanie]

In MLOps, experiment tracking and metadata management are closely related but distinct concepts. Experiment tracking is about keeping track of the details of each experiment you run, such as the hyperparameters, training data, and evaluation metrics, so that you can reproduce and iterate on your results. Metadata management, on the other hand, is about keeping track of the context and dependencies of your models and pipelines, such as the data sources, code versions, and infrastructure configurations, so that you can understand and manage the entire lifecycle of your models.

[MariaKaterin]

Tracking experiments and storing metadata help keep things organized, especially when dealing with many models. I like using automated logging and custom tags to make searching easier. What challenges are you facing?

[GiovanniGiorno7]

Keeping track of experiments and metadata can get messy fast, especially when dealing with multiple runs and configurations. I’ve found that setting up a structured pipeline with clear tagging and versioning really helps. Using automation to log parameters and results saves a ton of time too. This reminds me of how digital asset tools for photography professionals handle cataloging—organization and easy retrieval make all the difference.