AWSML

In the era of big data and AI, companies are continually seeking ways to use these technologies to gain a competitive edge. One of the hottest areas in AI right now is generative AI, and for good reason. Generative AI offers powerful solutions that push the boundaries of what’s possible in terms of creativity and … Read more

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

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

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

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

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

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

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

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

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

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

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

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

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

This is Part 3 of our series where we design and implement an MLOps pipeline for visual quality inspection at the edge. In this post, we focus on how to automate the edge deployment part of the end-to-end MLOps pipeline. We show you how to use AWS IoT Greengrass to manage model inference at the … Read more

This guest post is co-written by Lydia Lihui Zhang, Business Development Specialist, and Mansi Shah, Software Engineer/Data Scientist, at Planet Labs. The analysis that inspired this post was originally written by Jennifer Reiber Kyle. Amazon SageMaker geospatial capabilities combined with Planet’s satellite data can be used for crop segmentation, and there are numerous applications and … Read more

This post is co-written with Ilan Geller and Shuyu Yang from Accenture. Enterprises today face major challenges when it comes to using their information and knowledge bases for both internal and external business operations. With constantly evolving operations, processes, policies, and compliance requirements, it can be extremely difficult for employees and customers to stay up … Read more

We’re excited to announce that Amazon SageMaker Canvas now offers a quicker and more user-friendly way to create machine learning models for time-series forecasting. SageMaker Canvas is a visual point-and-click service that enables business analysts to generate accurate machine learning (ML) models without requiring any machine learning experience or having to write a single line of code. SageMaker … Read more

In the world of data-driven decision-making, time series forecasting is key in enabling businesses to use historical data patterns to anticipate future outcomes. Whether you are working in asset risk management, trading, weather prediction, energy demand forecasting, vital sign monitoring, or traffic analysis, the ability to forecast accurately is crucial for success. In these applications, … Read more

In the rapidly evolving world of AI and machine learning (ML), foundation models (FMs) have shown tremendous potential for driving innovation and unlocking new use cases. However, as organizations increasingly harness the power of FMs, concerns surrounding data privacy, security, added cost, and compliance have become paramount. Regulated and compliance-oriented industries, such as financial services, … Read more

Companies use time series forecasting to make core planning decisions that help them navigate through uncertain futures. This post is meant to address supply chain stakeholders, who share a common need of determining how many finished goods are needed over a mixed variety of planning time horizons. In addition to planning how many units of … Read more

From startups to enterprises, organizations of all sizes are getting started with generative AI. They want to capitalize on generative AI and translate the momentum from betas, prototypes, and demos into real-world productivity gains and innovations. But what do organizations need to bring generative AI into the enterprise and make it real? When we talk … Read more

The Amazon EU Design and Construction (Amazon D&C) team is the engineering team designing and constructing Amazon Warehouses across Europe and the MENA region. The design and deployment processes of projects involve many types of Requests for Information (RFIs) about engineering requirements regarding Amazon and project-specific guidelines. These requests range from simple retrieval of baseline … Read more

MDaudit provides a cloud-based billing compliance and revenue integrity software as a service (SaaS) platform to more than 70,000 healthcare providers and 1,500 healthcare facilities, ensuring healthcare customers maintain regulatory compliance and retain revenue. Working with the top 60+ US healthcare networks, MDaudit needs to be able to scale its artificial intelligence (AI) capabilities to … Read more

As machine learning (ML) goes mainstream and gains wider adoption, ML-powered inference applications are becoming increasingly common to solve a range of complex business problems. The solution to these complex business problems often requires using multiple ML models and steps. This post shows you how to build and host an ML application with custom containers … Read more

This post was co-authored with Daniele Chiappalupi, participant of the AWS student Hackathon team at ETH Zürich. Everyone can easily get started with machine learning (ML) using Amazon SageMaker JumpStart. In this post, we show you how a university Hackathon team used SageMaker JumpStart to quickly build an application that helps users identify and remove … Read more

We’re at an exciting inflection point in the widespread adoption of machine learning (ML), and we believe most customer experiences and applications will be reinvented with generative AI. Generative AI can create new content and ideas, including conversations, stories, images, videos, and music. Like most AI, generative AI is powered by ML models—very large models … Read more

Reinforcement Learning from Human Feedback (RLHF) is recognized as the industry standard technique for ensuring large language models (LLMs) produce content that is truthful, harmless, and helpful. The technique operates by training a “reward model” based on human feedback and uses this model as a reward function to optimize an agent’s policy through reinforcement learning … Read more

In this post, we discuss how United Airlines, in collaboration with the Amazon Machine Learning Solutions Lab, build an active learning framework on AWS to automate the processing of passenger documents. “In order to deliver the best flying experience for our passengers and make our internal business process as efficient as possible, we have developed … Read more

The adoption of generative AI is rapidly expanding, reaching an ever-growing number of industries and users worldwide. With the increasing complexity and scale of generative AI models, it is crucial to work towards minimizing their environmental impact. This involves a continuous effort focused on energy reduction and efficiency by achieving the maximum benefit from the … Read more