Combining structured and unstructured data for ML is challenging because of the inherent differences between these two types of data.
đź’ˇData version control systems help teams handle it all via a unified platform and work easily with both structured and unstructured data using familiar Git-like commands and workflows.
Why is this important?
Businesses have more unstructured data than ever. While this data carries a massive opportunity for business insights and ML applications, putting this data to good use is hard.
How do you move data without disrupting usersâť“ How do you gain visibility into unstructured dataâť“ What about the legal constraintsâť“And how do you combine it with structured dataâť“
Every data practitioner who dabbles in ML will need to find the answer to this question sooner or later.
What comes next
Check out this guide to learn how data version control systems can be used to solve real-world ML problems: Managing Structured and Unstructured Data – a Guide for an Effective Synergy
What other people are saying about it
A deep dive into the value of unstructured data: Unstructured Data – The Unsung Hero of Machine Learning
