1513-Lessons Learned from Applying Deep Learning in Real-World Software Systems

Deep Learning has proven to be a powerful tool across industries, but implementing it successfully in production environments presents significant challenges. This talk shares practical lessons learned from multiple real-world applications of deep learning — including computer vision systems for hardware validation, AI assistants for SaaS platforms, and fine-tuned language models for text analysis and generation. We’ll explore how traditional software engineering practices evolve when integrating convolutional and transformer-based architectures, and how factors such as data imbalance, overfitting, and limited datasets affect outcomes. The session will also discuss strategies to mitigate these issues, such as data augmentation, ensemble methods, and the use of retrieval-augmented generation (RAG). Attendees will gain a deeper understanding of the end-to-end process of developing AI-enabled solutions — from problem definition and data acquisition to model deployment and continuous improvement.

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