Joshua Arvin Lat
Joshua Arvin Lat is the Chief Technology Officer (CTO) of NuWorks Interactive Labs, Inc. He previously served as the CTO of 3 Australian-owned companies and also served as the Director for Software Development and Engineering for multiple e-commerce startups in the past. Years ago, he and his team won 1st place in a global cybersecurity competition with their published research paper. He is also an AWS Machine Learning Hero and he has been sharing his knowledge in several international conferences to discuss practical strategies on machine learning, engineering, security, and management. He is also the author of the books "Machine Learning with Amazon SageMaker Cookbook", "Machine Learning Engineering on AWS", and "Building and Automating Penetration Testing Labs in the Cloud". Due to his proven track record in leading digital transformation within organizations, he has been recognized as one of the prestigious Orange Boomerang: Digital Leader of the Year 2023 award winners.
Sessions
These last few years, we’ve seen a significant increase in the number of Generative AI-powered applications being developed by organizations and professionals globally. Due to the number of possible variations available when building these types of systems, companies often struggle to identify and implement the most effective approach. After a few weeks of running their Gen AI application, they start to encounter issues and challenges related to performance, scalability, and cost management.
In this session, we will discuss various best practices and strategies when building, scaling, and optimizing Generative AI systems. We'll tackle various challenges that organizations experience after they have deployed their first version of the Gen AI application and we'll discuss multiple optimization strategies to reduce costs and improve the application's performance.
Despite the significant increase in the number of Generative AI-powered applications being developed by companies and professionals globally, many organizations are unable to secure their deployed applications properly. One of the practical techniques to secure Gen AI systems along with self-hosted LLMs involves building a vulnerability scanner that checks for vulnerabilities such as prompt injection. In this session, we will discuss how to build a custom scanner to help teams identify security issues specific to their self-hosted LLMs.
These last few years, we’ve seen a significant increase in the number of serverless generative AI-powered applications being developed by organizations and professionals globally. Unfortunately, companies are unable to keep up with the security considerations and requirements and often end up unprepared for various types of attacks. There are different ways to attack serverless Generative AI-powered systems and most organizations are not equipped with the skills to secure these systems. In this talk, we will talk about the different ways these systems can be attacked and then we will share relevant strategies to protect these systems.