AWS Machine Learning Blog Amazon SageMaker Canvas allows you to use machine learning (ML) to generate predictions without having to write any code. It does so by covering the end-to-end ML workflow: whether you’re looking for powerful data preparation and AutoML, managed endpoint deployment, simplified MLOps capabilities, or the ability to configure foundation models for […]Continue reading
AWS Machine Learning Blog Retrieval Augmented Generation (RAG) allows you to provide a large language model (LLM) with access to data from external knowledge sources such as repositories, databases, and APIs without the need to fine-tune it. When using generative AI for question answering, RAG enables LLMs to answer questions with the most relevant, up-to-date […]Continue reading
AWS Machine Learning Blog Amazon Lex is a service that allows you to quickly and easily build conversational bots (“chatbots”), virtual agents, and interactive voice response (IVR) systems for applications such as Amazon Connect. Artificial intelligence (AI) and machine learning (ML) have been a focus for Amazon for over 20 years, and many of the […]Continue reading
AWS Machine Learning Blog Fine-tuning large language models (LLMs) allows you to adjust open-source foundational models to achieve improved performance on your domain-specific tasks. In this post, we discuss the advantages of using Amazon SageMaker notebooks to fine-tune state-of-the-art open-source models. We utilize Hugging Face’s parameter-efficient fine-tuning (PEFT) library and quantization techniques through bitsandbytes to […]Continue reading
AWS Machine Learning Blog Amazon SageMaker Serverless Inference allows you to serve model inference requests in real time without having to explicitly provision compute instances or configure scaling policies to handle traffic variations. You can let AWS handle the undifferentiated heavy lifting of managing the underlying infrastructure and save costs in the process. A Serverless […]Continue reading
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