C2 TF data validator with non-structured data

[jelena_m]

Hello,

I really enjoyed seeing the power of TF data validator on a diabetes dataset and how many options it gives for data visualization (and how quickly one can arrive to that point). It is now clear to me how the tool works with structured data, where we have clearly defined features (for which we can also generate schema, etc).

I was wondering if and how can the same concepts be used in the context of non-structured data, e.g. images or speech recognition. Any thoughts on this or interesting use cases where we can see this applied?

Thanks in advance,
Jelena

[Johnny56]

I asked a very similar question just now. Sorry, I did not find your question.

However, I would LOVE to know how to approach these scenarios.

[mjsmid]

Hi @jelena_m ,
Thanks for your question and welcome to discourse.
There was a previous post on this topic, so hopefully that answers your question;

What would be the lifecycle of unstructured data?

Hello, in the course, a typical data life cycle is described as: [image] Which totally makes sense for structured data. But I was wondering how can this transfer to unstructured data such as images. I believe the need for tracking is equally important. More specifically, what would be the statistics and schema for example in this case? Also how can we define anormalies? Data drift? If I take the phone scratch example that Andrew presented in course 1, if the light exposure diminish over ti…

Happy learning

Maarten