AWS Machine Learning Blog

Category: Foundational (100)

Architecture of AWS Field Advisor using Amazon Q Business

How AWS sales uses Amazon Q Business for customer engagement

In April 2024, we launched our AI sales assistant, which we call Field Advisor, making it available to AWS employees in the Sales, Marketing, and Global Services organization, powered by Amazon Q Business. Since that time, thousands of active users have asked hundreds of thousands of questions through Field Advisor, which we have embedded in our customer relationship management (CRM) system, as well as through a Slack application.

Figure 2: Depicting high level architecture of Tecton & SageMaker showing end-to-end feature lifecycle

Real value, real time: Production AI with Amazon SageMaker and Tecton

In this post, we discuss how Amazon SageMaker and Tecton work together to simplify the development and deployment of production-ready AI applications, particularly for real-time use cases like fraud detection. The integration enables faster time to value by abstracting away complex engineering tasks, allowing teams to focus on building features and use cases while providing a streamlined framework for both offline training and online serving of ML models.

Enabling production-grade generative AI: New capabilities lower costs, streamline production, and boost security

Enabling production-grade generative AI: New capabilities lower costs, streamline production, and boost security

As generative AI moves from proofs of concept (POCs) to production, we’re seeing a massive shift in how businesses and consumers interact with data, information—and each other. In what we consider “Act 1” of the generative AI story, we saw previously unimaginable amounts of data and compute create models that showcase the power of generative […]

AI21 Labs Jamba-Instruct model is now available in Amazon Bedrock

We are excited to announce the availability of the Jamba-Instruct large language model (LLM) in Amazon Bedrock. Jamba-Instruct is built by AI21 Labs, and most notably supports a 256,000-token context window, making it especially useful for processing large documents and complex Retrieval Augmented Generation (RAG) applications. What is Jamba-Instruct Jamba-Instruct is an instruction-tuned version of […]

Boost your content editing with Contentful and Amazon Bedrock

This post is co-written with Matt Middleton from Contentful. Today, jointly with Contentful, we are announcing the launch of the AI Content Generator powered by Amazon Bedrock. The AI Content Generator powered by Amazon Bedrock is an app available on the Contentful Marketplace that allows users to create, rewrite, summarize, and translate content using cutting-edge […]

Two graphs for timeseries. The top shows the timeseries for motor temperatures and motor speeds. The lower graph shows the anomaly score over time with three peaks that indicate anomalies..

Detect anomalies in manufacturing data using Amazon SageMaker Canvas

With the use of cloud computing, big data and machine learning (ML) tools like Amazon Athena or Amazon SageMaker have become available and useable by anyone without much effort in creation and maintenance. Industrial companies increasingly look at data analytics and data-driven decision-making to increase resource efficiency across their entire portfolio, from operations to performing […]

Driving advanced analytics outcomes at scale using Amazon SageMaker powered PwC’s Machine Learning Ops Accelerator

This post was written in collaboration with Ankur Goyal and Karthikeyan Chokappa from PwC Australia’s Cloud & Digital business. Artificial intelligence (AI) and machine learning (ML) are becoming an integral part of systems and processes, enabling decisions in real time, thereby driving top and bottom-line improvements across organizations. However, putting an ML model into production […]

Reduce model deployment costs by 50% on average using the latest features of Amazon SageMaker

As organizations deploy models to production, they are constantly looking for ways to optimize the performance of their foundation models (FMs) running on the latest accelerators, such as AWS Inferentia and GPUs, so they can reduce their costs and decrease response latency to provide the best experience to end-users. However, some FMs don’t fully utilize […]