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From AI to Data Engineering: How My Journey Taught Me That ‘Data is Everything’
- The author's journey from studying AI & ML in B.Tech to realizing the importance of data engineering is highlighted.
- Despite the lack of AI focus in the B.Tech syllabus, the author self-taught AI concepts but skipped mathematical fundamentals initially.
- The challenges faced during internships at KHMDL and RBG.AI led to the realization that deeper understanding of AI was necessary.
- The turning point came with a lecture on traditional AI teaching methods focusing on math and statistics by a Phosphene AI Co-Founder.
- This led to a shift in approach: creating custom activation functions, analyzing data distributions, and thinking more like a mathematician.
- The author participated in hackathons, like the Smart India Hackathon, where a Mental Health Web Application named ReboundX was developed.
- Venturing into Frontend Development, the author used React.js to enhance AI models and later collaborated on a Facial Emotion Detection Dashboard.
- The experience with real-world projects like Revealix.ai showed the importance of frontend in enhancing AI usability and user experience.
- A pivotal moment led the author to pivot from AI to Data Engineering, understanding the critical role of structured data in successful AI models.
- By transitioning to Data Engineering, the author grasped the importance of data pipelines, data structuring, and the convergence of engineering and analytics.
- Ultimately, the journey emphasizes that irrespective of AI or Data Engineering, mastering data is fundamental for success in both fields.
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