-
Streamline machine learning workflows from local development to scalable experiments by using SageMaker AI and Hydra
Simple config management and a unified ML developer experience across environments and project stage.
-
Democratizing GenAI through a Global Enterprise Portal
Learn how to democratize GenAI in your organization using the Global Enterprise Portal (GEP) framework.
-
Symbolic AI and Foundation Models Integration towards Reliable and Trustworthy Industry-grade AI Systems
The integration of symbolic AI and foundation models is a promising direction for developing reliable and trustworthy domain-specific industry-grade AI systems. Symbolic AI offers strong reasoning capabilities, interpretability, and the ability to incorporate domain knowledge, whilst foundation models provide powerful learning and generalisation abilities. However, effectively combining these two paradigms presents significant challenges. This panel will bring together research, AI systems, and industry strategy perspectives into discussion and debate about the current state of symbolic AI. Topics covered include foundation model integration, key technical and practical hurdles, and suggested paths forward for deploying such hybrid systems in real-world industrial applications. The panelists will share their perspectives on the complementary strengths of symbolic AI and foundation models, and how they can be seamlessly integrated to create AI solutions that are more robust, transparent, and aligned with human values. They will also explore the business implications of this technological convergence, including the impact on product development, deployment, and customer trust. Through an interactive discussion, the panel aims to provide the audience with a comprehensive understanding of the opportunities and challenges in symbolic AI and foundation models integration, as well as practical insights that can guide the development of the next generation of reliable and trustworthy industrial AI systems.
-
Hyperparameter optimization for quantum machine learning with Amazon Braket
Use best practices from machine learning to improve quantum experiment management and hyperparameter optimization.
-
Build and train computer vision models to detect car positions in images using Amazon SageMaker and Amazon Rekognition
Quickly build an algorithm to detect the position of cars without complex custom models and fine-tuning, by using clever post-processing.