Day 1

In this session, I will explore how intelligent training data pipelines are the key to scaling computer vision AI effectively. Imagine the challenge of needing millions of accurately labeled images for a reliable AI model—without the right infrastructure, this can become a bottleneck. I will deep dive into how intelligent pipelines, driven by automation technologies like AI-assisted labeling and active learning, replace tedious manual workflows, drastically boosting efficiency and data quality. Through real-world examples, I will showcase how these pipelines can accelerate annotation processes and improve model accuracy. Engineers will leave with actionable insights on implementing semi-supervised labeling, quality checks, and advanced tools to jumpstart their AI projects with smarter data pipelines.

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.