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Deploying models at scale can be a cumbersome task for many data scientists and machine learning engineers. However, Amazon SageMaker endpoints provide a simple solution for deploying and scaling your machine learning (ML) model inferences. Our last blog post and GitHub repo on hosting a YOLOv5 TensorFlowModel on Amazon SageMaker Endpoints sparked a lot of interest …

Read more “Hosting YOLOv8 PyTorch models on Amazon SageMaker Endpoints”

Preparing a retailer’s online catalog once required expensive physical photoshoots to capture products from every angle. A Tel Aviv startup is saving brands time and money by transforming these camera clicks into mouse clicks. Hexa uses GPU-accelerated computing to help companies turn their online inventory into 3D renders that shoppers can view in 360 degrees, …

Read more “AI Before You Buy: Israeli Startup Renders 3D Product Models for Top Retailers”

Posted by Yu Zhang, Research Scientist, and James Qin, Software Engineer, Google Research Last November, we announced the 1,000 Languages Initiative, an ambitious commitment to build a machine learning (ML) model that would support the world’s one thousand most-spoken languages, bringing greater inclusion to billions of people around the globe. However, some of these languages …

Read more “Universal Speech Model (USM): State-of-the-art speech AI for 100+ languages”

Language models are statistical methods predicting the succession of tokens in sequences, using natural text. Large language models (LLMs) are neural network-based language models with hundreds of millions (BERT) to over a trillion parameters (MiCS), and whose size makes single-GPU training impractical. LLMs’ generative abilities make them popular for text synthesis, summarization, machine translation, and …

Read more “Training large language models on Amazon SageMaker: Best practices”