THE ART OF FUTURE ENGINEERING: CRAFTING DATA FOR BETTER MODELS

This exploration, "The Art of Future Engineering: Crafting Data for Better Models," ventures beyond the surface level of data collection and storage to delve into the core principles and creative processes that define modern data engineering for AI. It recognizes that data is not a static entity but a dynamic material, capable of being sculpted, enriched, and strategically deployed to optimize the learning process of our models. Just as a master craftsman understands the properties of their materials, the future data engineer must possess a deep understanding of data characteristics, its inherent biases, its potential for transformation, and the ethical considerations that govern its use.While engineering provides the structural framework, the true art of crafting data for better models lies in the creative and iterative processes of transformation and refinement. Moving beyond the raw data requires a keen understanding of the underlying patterns and relationships, often demanding a level of intuitive insight that complements technical expertise.

Autorentext

Dr.A.Banushri is working as Associate Professor in Department of CSE in VISTAS Chennai.Dr.F.Benasir Begam is working as Assistant Professor in Department of CSE in VISTAS Chennai.Dr.K.Ulaga priya is working as Associate professor in Department of CSE in VISTAS Chennai.

Klappentext

This exploration, "The Art of Future Engineering: Crafting Data for Better Models," ventures beyond the surface level of data collection and storage to delve into the core principles and creative processes that define modern data engineering for AI. It recognizes that data is not a static entity but a dynamic material, capable of being sculpted, enriched, and strategically deployed to optimize the learning process of our models. Just as a master craftsman understands the properties of their materials, the future data engineer must possess a deep understanding of data characteristics, its inherent biases, its potential for transformation, and the ethical considerations that govern its use.While engineering provides the structural framework, the true art of crafting data for better models lies in the creative and iterative processes of transformation and refinement. Moving beyond the raw data requires a keen understanding of the underlying patterns and relationships, often demanding a level of intuitive insight that complements technical expertise.