This week, I’m really happy to be able to continue the conversation we’ve been having with our users and community around the role of data annotation in MLOps.

We were lucky to get to talk to Iva Gumnishka, the founder of Humans in the Loop. They are an organization that provides data annotation and collection services. Their teams are primarily made up of those who have been affected by conflict and now are asylum seekers or refugees.

Iva and her team has a ton of experience working with annotation and has seen how different companies build this into their production machine learning lifecycles. We’re continuing to work on a feature that will allow you to do this as part of your MLOps workflow when using ZenML, and I welcome any feedback you might have on the back of this podcast or the articles we’ve been publishing on the ZenML blog. Please do drop in to our Slack community if you have thoughts on this!

In this clip, speaks about where and when data labeling takes place in the machine learning workflows and lifecycles that she’s observed:

Since she and her team have used so many of them, I also asked Iva to give her take on the annotation tools that she’s excited about. That part of the conversation is worth pairing with the Humans in the Loop blog where you can find many in-depth reviews of these tools.

If you’re interested in tools, also be sure to check out our awesome-open-data-annotation repository where we’ve gathered together the best open-source annotation tools available at the moment.

As always, full show notes and links are available on our dedicated podcast page.


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