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Apache Kafka is a high-performance, highly scalable event streaming platform. To unlock Kafka’s full potential, you need to carefully consider the design of your application. It’s all too easy to write Kafka applications that perform poorly or eventually hit a scalability brick wall. Since 2015, IBM has provided the IBM Event Streams service, which is …
Read more “Five scalability pitfalls to avoid with your Kafka application”
Posted by Katherine Heller, Research Scientist, Google Research, on behalf of the CAIR Team Artificial intelligence (AI) and related machine learning (ML) technologies are increasingly influential in the world around us, making it imperative that we consider the potential impacts on society and individuals in all aspects of the technology that we create. To these …
Read more “Responsible AI at Google Research: Context in AI Research (CAIR)”
Building out a machine learning operations (MLOps) platform in the rapidly evolving landscape of artificial intelligence (AI) and machine learning (ML) for organizations is essential for seamlessly bridging the gap between data science experimentation and deployment while meeting the requirements around model performance, security, and compliance. In order to fulfill regulatory and compliance requirements, the …
Editorial note: Whether you’re new to Google Cloud or an experienced user, read on to learn how Duet AI can help you learn about products and services, generate code and commands, and understand your environment. In fast-moving organizations, engineers often work on large projects that utilize hundreds of cloud products and services, spanning multiple teams …
Read more “3 new ways Duet AI can help you get things done fast in the Google Cloud console”
*= Equal Contributors We propose a Self-supervised Anomaly Detection technique, called SeMAnD, to detect geometric anomalies in Multimodal geospatial datasets. Geospatial data comprises acquired and derived heterogeneous data modalities that we transform to semantically meaningful, image-like tensors to address the challenges of representation, alignment, and fusion of multimodal data. SeMAnD is comprised of (i) a …
Read more “SeMAnD: Self-Supervised Anomaly Detection in Multimodal Geospatial Datasets”
Authors:Bruce Wobbe, Leticia Kwok Additional Credits:Sanford Holsapple, Eugene Lok, Jeremy Kelly Introduction At Netflix, we strive to give our members an excellent personalized experience, helping them make the most successful and satisfying selections from our thousands of titles. We already personalize artwork and trailers, but we hadn’t yet personalized sizzle reels — until now. A sizzle reel is a montage …
Read more “The Next Step in Personalization: Dynamic Sizzles”
At the beginning of the year, we laid out a new strategy for IBM Power under the leadership of Ken King, who will be retiring by the end of 2023 after forty years with IBM. It is with immense gratitude that I thank Ken for his leadership not only across IBM Power, but for his …
Read more “Building on a year of focus to help IBM Power clients grow with hybrid cloud and AI”
This post is cowritten with Ming (Melvin) Qin, David Bericat and Brad Genereaux from NVIDIA. Medical imaging AI researchers and developers need a scalable, enterprise framework to build, deploy, and integrate their AI applications. AWS and NVIDIA have come together to make this vision a reality. AWS, NVIDIA, and other partners build applications and solutions …
Read more “Build a medical imaging AI inference pipeline with MONAI Deploy on AWS”
AI models continue to get bigger, requiring larger compute clusters with exa-FLOPs (10^18 FLOPs) of computing. While large-scale models continue to unlock new capabilities, driving down the cost of training and serving these models is the key to sustaining the pace of this innovation. Typically, the tensor operations (ops)1 are the most compute-intensive part of …
Read more “Introducing Accurate Quantized Training (AQT) for accelerated ML training on TPU v5e”
Posted by Xin Wang, Software Engineer, and Nishanth Dikkala, Research Scientist, Google Research Contemporary deep learning models have been remarkably successful in many domains, ranging from natural language to computer vision. Transformer neural networks (transformers) are a popular deep learning architecture that today comprise the foundation for most tasks in natural language processing and also …