As organizations scale their AI infrastructure to support trillion-parameter models, they face a difficult trade-off: reduced training time with lower cost or faster training time with a higher cost. When they checkpoint frequently to speed up recovery time and minimize lost training time, they incur in substantially higher storage cost.Continue Reading
Amazon Q Business is a generative AI-powered assistant for interacting with organizational knowledge and enterprise systems. In addition to providing built-in connectors and plug-ins to connect seamlessly to over 40 popular enterprise systems, Amazon Q Business provides the ability to interact seamlessly with other third-party applications using custom plugins. SomeContinue Reading
Picture this: your machine learning (ML) team has a promising model to train and experiments to run for their generative AI project, but they’re waiting for GPU availability. The ML scientists spend time monitoring instance availability, coordinating with teammates over shared resources, and managing infrastructure allocation. Simultaneously, your infrastructure administratorsContinue Reading
Although rapid generative AI advancements are revolutionizing organizational natural language processing tasks, developers and data scientists face significant challenges customizing these large models. These hurdles include managing complex workflows, efficiently preparing large datasets for fine-tuning, implementing sophisticated fine-tuning techniques while optimizing computational resources, consistently tracking model performance, and achieving reliable,Continue Reading
Today we are excited to introduce the Text Ranking and Question and Answer UI templates to SageMaker AI customers. The Text Ranking template enables human annotators to rank multiple responses from a large language model (LLM) based on custom criteria, such as relevance, clarity, or factual accuracy. This ranked feedbackContinue Reading
Modern generative AI model providers require unprecedented computational scale, with pre-training often involving thousands of accelerators running continuously for days, and sometimes months. Foundation Models (FMs) demand distributed training clusters — coordinated groups of accelerated compute instances, using frameworks like PyTorch — to parallelize workloads across hundreds of accelerators (likeContinue Reading
This post is based on a technical report written by Kazuki Fujii, who led the Llama 3.3 Swallow model development. The Institute of Science Tokyo has successfully trained Llama 3.3 Swallow, a 70-billion-parameter large language model (LLM) with enhanced Japanese capabilities, using Amazon SageMaker HyperPod. The model demonstrates superior performanceContinue Reading
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 to speed up operator inspections andContinue Reading
Training a frontier model is highly compute-intensive, requiring a distributed system of hundreds, or thousands, of accelerated instances running for several weeks or months to complete a single job. For example, pre-training the Llama 3 70B model with 15 trillion training tokens took 6.5 million H100 GPU hours. On 256Continue Reading
In this post, we explore how you can use Amazon Bedrock to generate high-quality categorical ground truth data, which is crucial for training machine learning (ML) models in a cost-sensitive environment. Generative AI solutions can play an invaluable role during the model development phase by simplifying training and test dataContinue Reading
