Data Mesh Radio
#285 Getting Depth and Value From Generative AI - In Data Mesh and in General - Zhamak's Corner 33
Jan 12, 2024
The podcast explores the current impact and challenges of generative AI within data mesh. It emphasizes the need for reliable access to data products for machine learning engineers. The role of generative AI in data discovery is also discussed, including its potential in generating data models and providing context for data products.
19:15
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Quick takeaways
- Generative AI has not yet proven to be a significant game changer, despite advancements in chatbots.
- To fully leverage generative AI, more metadata around data products is needed for improved insights and value.
Deep dives
Generative AI and its Limitations
In this episode, Jean-Mack discusses the limitations of generative AI and its application in delivering deep value. While there have been advancements in chatbots, Jean-Mack points out that there has not been a significant transformation in people's ability to perform deep work. The focus should be on giving data developers easy and reliable access to data products, reducing the time spent on non-value adding tasks. Another challenge is the limited metadata available around data products, which hinders the full leverage of generative AI. Overall, while there is potential for generative AI, more data and improved data access are needed to unlock its value.
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