Thank you, @psp for this really thorough feedback. That’s not marketing-speak either! Really thankful.
(context: I’m the Product Manager that supports the MLMD team)
You’re right that the API docs are precise/abstract and not the quickest to scan and learn for someone new. That’s because MLMD’s users have mostly been other ML Infrastructure engineers, e.g. TensorFlow Extended (TFX).
Our goal with MLMD is to (1) make sure we record ~all that happens in ML pipelines over time and (2) make it useful and accessible afterwards. I.e. write it all down and then let you run reports, analyses, etc. automatically. Maybe you want to see all the other pipelines at your company that trained on the same dataset,  and sort the models by best loss, etc. You can see an experimental project called NitroML 1 that uses MLMD this way. Note, that’s an experimental project and I wouldn’t necessarily use them as a reference implementation!
This is, admittedly, rather general: a ~graph datastore for arbitrary directed-acyclic-graphs with some ML semantics thrown in.
In the meantime, here are some very cursory analogies that might help:
MLMD is sort of a grammar for ML.


Artifacts are nouns. A model, a dataset, a model analysis result, etc.

Executions are verbs. ingesting training data, transforming it, training the model, analyzing the model

Contexts are somewhat like sentences. E.g. a DAG of artifacts and executions linked together can be a Context.
a component is, for example, a TFX Component like the Trainer 1. A component is specific to TFX. MLMD is slightly more general. So when TFX uses MLMD, a component run is recorded as an execution in MLMD. But if MLMD is used outside TFX, not all MLMD executions are from TFX components. The docs don’t say that we mean a TFX component, though. You’re right to point that out.

I’ve added a task for us (the MLMD team) to improve the docs for a reader who doesn’t necessarily already have years of experience working on these problems already, and to clarify what we mean by component (and any other non-MLMD-specific definitions).