This blog introduces fully managed MLflow 3.0 on Amazon SageMaker AI, which accelerates Generative AI development by unifying experiment tracking, behavior monitoring, and model lifecycle management into a single platform. This version adds tracing and version tracking features, enabling developers to capture inputs, outputs, and metadata for easier debugging and performance optimization. With deep integration into Amazon Bedrock and SageMaker HyperPod, MLflow 3.0 enhances observability, speeds up troubleshooting, and shortens the time to production for AI models.
This blog describes how S2 Labs, Empact AI, and Kraken Robotics use physics-informed AI on AWS HPC to enhance subsurface infrastructure mapping. This approach combines magnetic imaging and deep learning (using a U-Net model) to accurately reconstruct underground or underwater structures such as pipes and tanks. Leveraging AWS Batch, EC2, and S3 for parallel computing, the system achieves high precision in detecting subsurface features up to 40 meters deep—significantly outperforming traditional mapping methods.
This blog explores how to unlock the full potential of Amazon Connect, an AI- and AWS-powered contact center platform. It highlights the importance of effective change management, including identifying the right stakeholders, securing an executive sponsor, building change ambassadors, and tracking meaningful performance metrics. Success relies on understanding business needs, targeted training, and strong internal communication. When properly implemented, Amazon Connect can enhance operations, enable automation, and deliver superior customer experiences—maximizing ROI and long-term value for organizations.