Machine learning models are trained on historical data and deployed in real-world environments. Machine learning models are trained on historical data and deployed in real-world environments. Read More
Quantization might sound like a topic reserved for hardware engineers or AI researchers in lab coats. Quantization might sound like a topic reserved for hardware engineers or AI researchers in lab coats. Read More
Learning disentangled representations from unlabelled data is a fundamental challenge in machine learning. Solving it may unlock other problems, such as generalization, interpretability, or fairness. Although remarkably challenging to solve in theory, disentanglement is often achieved in practice through prior matching. Furthermore, recent works have… Read More »Disentangled Representational Learning with the Gromov-Monge Gap Apple Machine Learning Research
[[{“value”:”Apple is presenting new research at the ACM annual conference on Human-Computer Interaction (CHI), which takes place in person in Yokohama, Japan, from April 26 to May 1. We are proud to again sponsor the conference, which brings together the scientific and industrial research communities… Read More »ACM Human-Computer Interaction Conference (CHI) 2025 Apple Machine Learning Research
Post Content Read More
[[{“value”:” As organizations scale their Amazon Elastic Kubernetes Service (Amazon EKS) deployments, platform administrators face increasing challenges in efficiently managing multi-tenant clusters. Tasks such as investigating pod failures, addressing resource constraints, and resolving misconfiguration can consume significant time and effort. Instead of spending valuable engineering… Read More »Automate Amazon EKS troubleshooting using an Amazon Bedrock agentic workflow Vikram Venkataraman AWS Machine Learning Blog
[[{“value”:” Businesses are increasingly seeking domain-adapted and specialized foundation models (FMs) to meet specific needs in areas such as document summarization, industry-specific adaptations, and technical code generation and advisory. The increased usage of generative AI models has offered tailored experiences with minimal technical expertise, and… Read More »Host concurrent LLMs with LoRAX John Kitaoka AWS Machine Learning Blog
[[{“value”:” Keeping an up-to-date asset inventory with real devices deployed in the field can be a challenging and time-consuming task. Many electricity providers use manufacturer’s labels as key information to link their physical assets within asset inventory systems. Computer vision can be a viable solution… Read More »Build a computer vision-based asset inventory application with low or no training Elisabetta Castellano AWS Machine Learning Blog
This post is divided into two parts; they are: • Contextual Keyword Extraction • Contextual Text Summarization Contextual keyword extraction is a technique for identifying the most important words in a document based on their contextual relevance. This post is divided into two parts; they are:… Read More »Applications with Context Vectors Muhammad Asad Iqbal Khan MachineLearningMastery.com