699: The Modern Data Stack — with Harry Glaser

BI Tools

Business intelligence (BI) tools like Looker and Tableau play a crucial role in monitoring deployed machine learning models. Harry Glaser explains that these tools allow data scientists and business users to track model performance through dashboards, providing insights into potential issues such as data drift or model inaccuracies 1. This integration enables non-technical stakeholders to interact with models, offering a hands-on approach to understanding and experimenting with model outputs.

You can actually use it also to play with a model. So this notion of just poking at it, playing with it, right. It doesn't sound rigorous, but it's something every data scientist does.

--- Harry Glaser

Jon Krohn highlights that these tools provide a user-friendly interface for exploring model configurations, making them accessible to a broader audience 2.

   

Collaboration

Collaborative data science platforms like Deepnote and Hex enhance the ability of data scientists to work together effectively. Jon Krohn notes that these cloud-based tools facilitate collaboration by integrating version control, CI/CD, and logging, which are essential for deploying machine learning models 3. Harry Glaser adds that these platforms allow business users to engage with models, fostering a collaborative environment where both technical and non-technical team members can experiment and innovate.

There's this hierarchy of needs, right? And so you supply the data scientists with something that makes their life better.

--- Harry Glaser

This approach not only accelerates model deployment but also democratizes access to data insights, enabling a more inclusive data-driven culture within organizations 4.