Amazon Textract’s new Layout feature introduces efficiencies in general purpose and generative AI document processing tasks

Amazon Textract is a machine learning (ML) service that automatically extracts text, handwriting, and data from any document or image. AnalyzeDocument Layout is a new feature that allows customers to automatically extract layout elements such as paragraphs, titles, subtitles, headers, footers, and more from documents. Layout extends Amazon Textract’s word and line detection by automatically … Read more

Use Amazon SageMaker Studio to build a RAG question answering solution with Llama 2, LangChain, and Pinecone for fast experimentation

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 information and optionally cite … Read more

KT’s journey to reduce training time for a vision transformers model using Amazon SageMaker

KT Corporation is one of the largest telecommunications providers in South Korea, offering a wide range of services including fixed-line telephone, mobile communication, and internet, and AI services. KT’s AI Food Tag is an AI-based dietary management solution that identifies the type and nutritional content of food in photos using a computer vision model. This … Read more

Moderate your Amazon IVS live stream using Amazon Rekognition

Amazon Interactive Video Service (Amazon IVS) is a managed live streaming solution that is designed to provide a quick and straightforward setup to let you build interactive video experiences and handles interactive video content from ingestion to delivery. With the increased usage of live streaming, the need for effective content moderation becomes even more crucial. … Read more

Retrieval-Augmented Generation with LangChain, Amazon SageMaker JumpStart, and MongoDB Atlas semantic search

Generative AI models have the potential to revolutionize enterprise operations, but businesses must carefully consider how to harness their power while overcoming challenges such as safeguarding data and ensuring the quality of AI-generated content. The Retrieval-Augmented Generation (RAG) framework augments prompts with external data from multiple sources, such as document repositories, databases, or APIs, to … Read more

Build a foundation model (FM) powered customer service bot with agents for Amazon Bedrock

From enhancing the conversational experience to agent assistance, there are plenty of ways that generative artificial intelligence (AI) and foundation models (FMs) can help deliver faster, better support. With the increasing availability and diversity of FMs, it’s difficult to experiment and keep up-to-date with the latest model versions. Amazon Bedrock is a fully managed service … Read more

Philips accelerates development of AI-enabled healthcare solutions with an MLOps platform built on Amazon SageMaker

This is a joint blog with AWS and Philips. Philips is a health technology company focused on improving people’s lives through meaningful innovation. Since 2014, the company has been offering customers its Philips HealthSuite Platform, which orchestrates dozens of AWS services that healthcare and life sciences companies use to improve patient care. It partners with … Read more

Fine-tune Whisper models on Amazon SageMaker with LoRA

Whisper is an Automatic Speech Recognition (ASR) model that has been trained using 680,000 hours of supervised data from the web, encompassing a range of languages and tasks. One of its limitations is the low-performance on low-resource languages such as Marathi language and Dravidian languages, which can be remediated with fine-tuning. However, fine-tuning a Whisper … Read more

Use foundation models to improve model accuracy with Amazon SageMaker

Photo by Scott Webb on Unsplash Determining the value of housing is a classic example of using machine learning (ML). A significant influence was made by Harrison and Rubinfeld (1978), who published a groundbreaking paper and dataset that became known informally as the Boston housing dataset. This seminal work proposed a method for estimating housing … Read more

Implement a custom AutoML job using pre-selected algorithms in Amazon SageMaker Automatic Model Tuning

AutoML allows you to derive rapid, general insights from your data right at the beginning of a machine learning (ML) project lifecycle. Understanding up front which preprocessing techniques and algorithm types provide best results reduces the time to develop, train, and deploy the right model. It plays a crucial role in every model’s development process … Read more

Best prompting practices for using the Llama 2 Chat LLM through Amazon SageMaker JumpStart

Llama 2 stands at the forefront of AI innovation, embodying an advanced auto-regressive language model developed on a sophisticated transformer foundation. It’s tailored to address a multitude of applications in both the commercial and research domains with English as the primary linguistic concentration. Its model parameters scale from an impressive 7 billion to a remarkable … Read more

Principal Financial Group uses AWS Post Call Analytics solution to extract omnichannel customer insights

An established financial services firm with over 140 years in business, Principal is a global investment management leader and serves more than 62 million customers around the world. Principal is conducting enterprise-scale near-real-time analytics to deliver a seamless and hyper-personalized omnichannel customer experience on their mission to make financial security accessible for all. They are … Read more

Foundational vision models and visual prompt engineering for autonomous driving applications

Prompt engineering has become an essential skill for anyone working with large language models (LLMs) to generate high-quality and relevant texts. Although text prompt engineering has been widely discussed, visual prompt engineering is an emerging field that requires attention. Visual prompts can include bounding boxes or masks that guide vision models in generating relevant and … Read more

Flag harmful content using Amazon Comprehend toxicity detection

Online communities are driving user engagement across industries like gaming, social media, ecommerce, dating, and e-learning. Members of these online communities trust platform owners to provide a safe and inclusive environment where they can freely consume content and contribute. Content moderators are often employed to review user-generated content and check that it’s safe and compliant … Read more

Fine-tune and Deploy Mistral 7B with Amazon SageMaker JumpStart

Today, we are excited to announce the capability to fine-tune the Mistral 7B model using Amazon SageMaker JumpStart. You can now fine-tune and deploy Mistral text generation models on SageMaker JumpStart using the Amazon SageMaker Studio UI with a few clicks or using the SageMaker Python SDK. Foundation models perform very well with generative tasks, … Read more

Implement real-time personalized recommendations using Amazon Personalize

At a basic level, Machine Learning (ML) technology learns from data to make predictions. Businesses use their data with an ML-powered personalization service to elevate their customer experience. This approach allows businesses to use data to derive actionable insights and help grow their revenue and brand loyalty. Amazon Personalize accelerates your digital transformation with ML, … Read more

Improve LLM responses in RAG use cases by interacting with the user

One of the most common applications of generative AI and large language models (LLMs) is answering questions based on a specific external knowledge corpus. Retrieval-Augmented Generation (RAG) is a popular technique for building question answering systems that use an external knowledge base. To learn more, refer to Build a powerful question answering bot with Amazon … Read more

Build trust and safety for generative AI applications with Amazon Comprehend and LangChain

We are witnessing a rapid increase in the adoption of large language models (LLM) that power generative AI applications across industries. LLMs are capable of a variety of tasks, such as generating creative content, answering inquiries via chatbots, generating code, and more. Organizations looking to use LLMs to power their applications are increasingly wary about … Read more

Personalize your generative AI applications with Amazon SageMaker Feature Store

Large language models (LLMs) are revolutionizing fields like search engines, natural language processing (NLP), healthcare, robotics, and code generation. The applications also extend into retail, where they can enhance customer experiences through dynamic chatbots and AI assistants, and into digital marketing, where they can organize customer feedback and recommend products based on descriptions and purchase … Read more

Build an image-to-text generative AI application using multimodality models on Amazon SageMaker

As we delve deeper into the digital era, the development of multimodality models has been critical in enhancing machine understanding. These models process and generate content across various data forms, like text and images. A key feature of these models is their image-to-text capabilities, which have shown remarkable proficiency in tasks such as image captioning … Read more

Improve prediction quality in custom classification models with Amazon Comprehend

Artificial intelligence (AI) and machine learning (ML) have seen widespread adoption across enterprise and government organizations. Processing unstructured data has become easier with the advancements in natural language processing (NLP) and user-friendly AI/ML services like Amazon Textract, Amazon Transcribe, and Amazon Comprehend. Organizations have started to use AI/ML services like Amazon Comprehend to build classification … Read more

Fast and cost-effective LLaMA 2 fine-tuning with AWS Trainium

Large language models (LLMs) have captured the imagination and attention of developers, scientists, technologists, entrepreneurs, and executives across several industries. These models can be used for question answering, summarization, translation, and more in applications such as conversational agents for customer support, content creation for marketing, and coding assistants. Recently, Meta released Llama 2 for both … Read more

Simplify medical image classification using Amazon SageMaker Canvas

Analyzing medical images plays a crucial role in diagnosing and treating diseases. The ability to automate this process using machine learning (ML) techniques allows healthcare professionals to more quickly diagnose certain cancers, coronary diseases, and ophthalmologic conditions. However, one of the key challenges faced by clinicians and researchers in this field is the time-consuming and … Read more

Create an HCLS document summarization application with Falcon using Amazon SageMaker JumpStart

Healthcare and life sciences (HCLS) customers are adopting generative AI as a tool to get more from their data. Use cases include document summarization to help readers focus on key points of a document and transforming unstructured text into standardized formats to highlight important attributes. With unique data formats and strict regulatory requirements, customers are … Read more

Automate prior authorization using CRD with CDS Hooks and AWS HealthLake

Prior authorization is a crucial process in healthcare that involves the approval of medical treatments or procedures before they are carried out. This process is necessary to ensure that patients receive the right care and that healthcare providers are following the correct procedures. However, prior authorization can be a time-consuming and complex process that requires … Read more

Code Llama code generation models from Meta are now available via Amazon SageMaker JumpStart

Today, we are excited to announce Code Llama foundation models, developed by Meta, are available for customers through Amazon SageMaker JumpStart to deploy with one click for running inference. Code Llama is a state-of-the-art large language model (LLM) capable of generating code and natural language about code from both code and natural language prompts. Code … Read more

Build an end-to-end MLOps pipeline for visual quality inspection at the edge – Part 1

A successful deployment of a machine learning (ML) model in a production environment heavily relies on an end-to-end ML pipeline. Although developing such a pipeline can be challenging, it becomes even more complex when dealing with an edge ML use case. Machine learning at the edge is a concept that brings the capability of running … Read more

Build an end-to-end MLOps pipeline for visual quality inspection at the edge – Part 2

In Part 1 of this series, we drafted an architecture for an end-to-end MLOps pipeline for a visual quality inspection use case at the edge. It is architected to automate the entire machine learning (ML) process, from data labeling to model training and deployment at the edge. The focus on managed and serverless services reduces … Read more