Hosting YOLOv8 PyTorch models on Amazon SageMaker Endpoints

Favorite 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

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Shared by AWS Machine Learning March 8, 2023

Training large language models on Amazon SageMaker: Best practices

Favorite 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

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Shared by AWS Machine Learning March 7, 2023

Achieve rapid time-to-value business outcomes with faster ML model training using Amazon SageMaker Canvas

Favorite Machine learning (ML) can help companies make better business decisions through advanced analytics. Companies across industries apply ML to use cases such as predicting customer churn, demand forecasting, credit scoring, predicting late shipments, and improving manufacturing quality. In this blog post, we’ll look at how Amazon SageMaker Canvas delivers faster and

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Shared by AWS Machine Learning March 4, 2023