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The Praxi Pod Room 101 : Unlocking the Power of AI: Data Classification & Curation Explained
Manage episode 507081226 series 3557343
Praxi Data Inc에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 Praxi Data Inc 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.
In this conversation, CEO Andrew Ahn discusses the intricacies of AI and data classification, emphasising the importance of data quality, curation, and the challenges posed by dark and gray data.
He highlights the risks of neglecting dark data and the benefits of automating data classification processes.
The discussion also covers real-world applications and the significance of domain knowledge in ensuring accurate data classification.
Takeaways
- The first step in creating an AI model is obtaining the right data.
- Data labelling, classification, and curation are distinct but interconnected processes.
- Curation is essential for organising data relevant to specific questions.
- Dark data represents unknown unknowns that can pose risks to businesses.
- Automating data classification can significantly reduce manual workload.
- 80% of a data worker's time is spent on data curation tasks.
- Bad data leads to poor decision-making and outcomes.
- Domain knowledge enhances the accuracy of data classification models.
- Companies need to be proactive in managing their dark data.
- The foundation of AI and analytics is high-quality, well-classified data.
Chapters
00:00 Introduction to AI and Data Classification
02:32 Understanding Data Labelling, Classification, and Curation
05:36 The Importance of Data Quality and Curation
08:09 Exploring Dark and Gray Data
11:07 The Risks of Ignoring Dark Data
13:54 Benefits of Automated Data Classification
16:18 Real-World Applications of Data Classification
19:20 The Role of Domain Knowledge in Data Classification
21:54 Conclusion and Future of Data Classification
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