Category: Amazon SageMaker

This post is co-written with Shamik Ray, Srivyshnav K S, Jagmohan Dhiman and Soumya Kundu from Twilio. Today’s leading companies trust Twilio’s Customer Engagement Platform (CEP) to build direct, personalized relationships with their customers everywhere in the world. Twilio enables companies to use communications and data to add intelligence and security to every step of […]

Photo by Zbynek Burival on Unsplash Time series forecasting is a specific machine learning (ML) discipline that enables organizations to make informed planning decisions. The main idea is to supply historic data to an ML algorithm that can identify patterns from the past and then use those patterns to estimate likely values about unseen periods […]

Starting with the AWS Neuron 2.18 release, you can now launch Neuron DLAMIs (AWS Deep Learning AMIs) and Neuron DLCs (AWS Deep Learning Containers) with the latest released Neuron packages on the same day as the Neuron SDK release. When a Neuron SDK is released, you’ll now be notified of the support for Neuron DLAMIs […]

In the ever-evolving landscape of machine learning and artificial intelligence (AI), large language models (LLMs) have emerged as powerful tools for a wide range of natural language processing (NLP) tasks, including code generation. Among these cutting-edge models, Code Llama 70B stands out as a true heavyweight, boasting an impressive 70 billion parameters. Developed by Meta […]

Today, we are excited to announce that the Jina Embeddings v2 model, developed by Jina AI, is available for customers through Amazon SageMaker JumpStart to deploy with one click for running model inference. This state-of-the-art model supports an impressive 8,192-tokens context length. You can deploy this model with SageMaker JumpStart, a machine learning (ML) hub […]

In today’s fast-paced corporate landscape, employee mental health has become a crucial aspect that organizations can no longer overlook. Many companies recognize that their greatest asset lies in their dedicated workforce, and each employee plays a vital role in collective success. As such, promoting employee well-being by creating a safe, inclusive, and supportive environment is […]

Today, we are excited to announce that the first model in the next generation Falcon 2 family, the Falcon 2 11B foundation model (FM) from Technology Innovation Institute (TII), is available through Amazon SageMaker JumpStart to deploy and run inference. Falcon 2 11B is a trained dense decoder model on a 5.5 trillion token dataset […]

Large multimodal models (LMMs) integrate multiple data types into a single model. By combining text data with images and other modalities during training, multimodal models such as Claude3, GPT-4V, and Gemini Pro Vision gain more comprehensive understanding and improved ability to process diverse data types. The multimodal approach allows models to handle a wider range […]

Mixture of Experts (MoE) architectures for large language models (LLMs) have recently gained popularity due to their ability to increase model capacity and computational efficiency compared to fully dense models. By utilizing sparse expert subnetworks that process different subsets of tokens, MoE models can effectively increase the number of parameters while requiring less computation per […]

This post shows you how to predict domain-specific product attributes from product images by fine-tuning a VLM on a fashion dataset using Amazon SageMaker, and then using Amazon Bedrock to generate product descriptions using the predicted attributes as input. So you can follow along, we’re sharing the code in a GitHub repository.