​ Researchers at Korea University have developed a new speech synthesizer called HierSpeech++. This research aims to create synthetic speech that is robust, expressive, natural, and human-like. The team aimed to achieve this without relying on a text-speech paired dataset and to improve existing models’… Read More »Researchers from Korea University Unveil HierSpeech++: A Groundbreaking AI Approach for High-Fidelity, Efficient Text-to-Speech and Voice Conversion Sana Hassan Artificial Intelligence Category – MarkTechPost

​ In the rapidly advancing fields of Artificial Intelligence (AI) and Machine Learning (ML), finding effective, automated, and adaptable approaches has become significantly crucial. The constant upliftment of AI and ML approaches has reshaped the possibilities of what machines can accomplish and how humans interact… Read More »Meet Relational Deep Learning Benchmark (RelBench): A Collection of Realistic, Large-Scale, and Diverse Benchmark Datasets for Machine Learning on Relational Databases Tanya Malhotra Artificial Intelligence Category – MarkTechPost

​ Amazon SageMaker is a fully managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning (ML) models at scale. SageMaker makes it easy to deploy models into production directly through API calls to the service. Models… Read More »Package and deploy classical ML and LLMs easily with Amazon SageMaker, part 2: Interactive User Experiences in SageMaker Studio Raghu Ramesha AWS Machine Learning Blog

​ Amazon SageMaker is a fully managed service that enables developers and data scientists to quickly and effortlessly build, train, and deploy machine learning (ML) models at any scale. SageMaker makes it straightforward to deploy models into production directly through API calls to the service.… Read More »Package and deploy classical ML and LLMs easily with Amazon SageMaker, part 1: PySDK Improvements Melanie Li AWS Machine Learning Blog

​ Today, we are excited to announce support for Code Editor, a new integrated development environment (IDE) option in Amazon SageMaker Studio. Code Editor is based on Code-OSS, Visual Studio Code Open Source, and provides access to the familiar environment and tools of the popular… Read More »New – Code Editor, based on Code-OSS VS Code Open Source now available in Amazon SageMaker Studio Eric Peña AWS Machine Learning Blog

​ As democratization of foundation models (FMs) becomes more prevalent and demand for AI-augmented services increases, software as a service (SaaS) providers are looking to use machine learning (ML) platforms that support multiple tenants—for data scientists internal to their organization and external customers. More and… Read More »Scale foundation model inference to hundreds of models with Amazon SageMaker – Part 1 Mehran Najafi AWS Machine Learning Blog

​ As organizations deploy models to production, they are constantly looking for ways to optimize the performance of their foundation models (FMs) running on the latest accelerators, such as AWS Inferentia and GPUs, so they can reduce their costs and decrease response latency to provide… Read More »Reduce model deployment costs by 50% on average using the latest features of Amazon SageMaker James Park AWS Machine Learning Blog

​ Amazon SageMaker makes it straightforward to deploy machine learning (ML) models for real-time inference and offers a broad selection of ML instances spanning CPUs and accelerators such as AWS Inferentia. As a fully managed service, you can scale your model deployments, minimize inference costs,… Read More »Minimize real-time inference latency by using Amazon SageMaker routing strategies James Park AWS Machine Learning Blog

​ Amazon SageMaker Canvas is a no-code workspace that enables analysts and citizen data scientists to generate accurate machine learning (ML) predictions for their business needs. Starting today, SageMaker Canvas supports advanced model build configurations such as selecting a training method (ensemble or hyperparameter optimization)… Read More »Build and evaluate machine learning models with advanced configurations using the SageMaker Canvas model leaderboard Janisha Anand AWS Machine Learning Blog

​ Building foundation models (FMs) requires building, maintaining, and optimizing large clusters to train models with tens to hundreds of billions of parameters on vast amounts of data. Creating a resilient environment that can handle failures and environmental changes without losing days or weeks of… Read More »Introducing Amazon SageMaker HyperPod to train foundation models at scale Brad Doran AWS Machine Learning Blog