HALL 3 - Tech Talks / Paper Presentations

Session will help you define the Platform features for a successful implementation of Ops for Data and identify the challenges

Organizations are overwhelmed by vast amounts of unstructured data—about 80% of enterprise information exists in formats like documents, emails, images, and PDFs. This untapped resource holds immense potential but remains challenging to process and analyze.

Traditional data pipelines struggle with unstructured data, but Generative AI (GenAI) and Large Language Models (LLMs) offer transformative solutions. This workshop will guide you through integrating GenAI into your data pipelines. We’ll address the challenges of unstructured data, explore how LLMs can help, and provide hands-on experience with OCR technology for AI ingestion. Whether you’re a data engineer, machine learning enthusiast, or IT professional, this workshop offers valuable insights and practical skills to enhance your data infrastructure.

In a world where over 90% of internet users speak languages other than English, the potential for growth lies in inclusivity. This talk will explore how AI is bridging the gap for over a billion users by transforming content into native languages at scale. I Himanshu Sharma, Co- Founder of Devnagri, will share insights on how neural machine translation, voice bots, and foundational language models are reshaping industries—creating opportunities for businesses to connect deeply with regional audiences. Dive into the future of AI-driven localization and learn how innovation is turning barriers into bridges for global communication.

This session explores how deep learning, particularly CNNs and advanced architectures, is transforming medical imaging. Drawing on research from IIT Hyderabad, it covers applications in detecting diseases through X-rays, MRIs, and CT scans, enhancing diagnostic accuracy. Key topics include challenges in data preparation, interpretability techniques to improve transparency, and how AI integrates into clinical workflows to support radiologists. Ethical considerations and future directions, such as privacy-preserving federated learning, highlight the responsible deployment of AI in healthcare.