November 20

AI is one of the hottest topics in tech today, but embedding it into systems is far from straightforward. It is not just about deploying a model and connecting it with LangChain4J. Key decisions need to be made about build methods, how to synchronize and secure data, and how to ensure proper monitoring for bias and drift. Without careful planning, integrating AI can lead to serious issues such as unfair outcomes, data security breaches, and unreliable predictions. The stakes are high, and getting it wrong can result in real-world consequences that impact trust and business outcomes.

In this hands-on session, Kyra Goud will guide participants as they learn how to avoid some of the common “burns” experienced when integrating AI into systems and how to safely embed an AI model into a platform. The session explores challenges such as governance, security, and monitoring while ensuring the system works reliably and fairly. By the end, attendees will walk away with a clear understanding of how to integrate AI effectively and responsibly in complex systems.

Your computer can understand the visual world around you—or even create a new one. From Face ID and Snapchat filters to Stable Diffusion, visual AI has become part of many everyday applications—and it’s easier to integrate than ever. Let’s discover the secrets behind computer vision and visual GenAI!

Starting from the basics of computer vision, we’ll explore how deep learning techniques can be leveraged to process, analyze, and alter visual information.

We’ll kick things off with a brief introduction to how images and videos are processed in computers. After that, we’ll take a deep dive into Convolutional Neural Networks (CNNs)—the type of neural network that revolutionized the field of computer vision. We’ll show you how different CNN architectures help master various computer vision tasks such as classification, segmentation, and pose detection.

Finally, we’ll explore GenAI and how diffusion models like Stable Diffusion generate and complete images. Throughout this learning experience, we’ll visualize theoretical concepts with live demos and showcase their applications in real-world business cases.

Can’t wait to get started on your own? We’ll introduce you to some open-source vision frameworks like MediaPipe and YOLO that you can try at home.

You will gain a framework for designing multi-step, agentic AI processes, along with practical skills to write and implement custom Cursor rules that maximize the reliability and effectiveness of AI in complex coding tasks.