AWS Machine Learning Blog

Significant new capabilities make it easier to use Amazon Bedrock to build and scale generative AI applications – and achieve impressive results

We introduced Amazon Bedrock to the world a little over a year ago, delivering an entirely new way to build generative artificial intelligence (AI) applications. With the broadest selection of first- and third-party foundation models (FMs) as well as user-friendly capabilities, Amazon Bedrock is the fastest and easiest way to build and scale secure generative […]

The future of productivity agents with NinjaTech AI and AWS Trainium

NinjaTech AI’s mission is to make everyone more productive by taking care of time-consuming complex tasks with fast and affordable artificial intelligence (AI) agents. We recently launched MyNinja.ai, one of the world’s first multi-agent personal AI assistants, to drive towards our mission. MyNinja.ai is built from the ground up using specialized agents that are capable of completing tasks on your behalf, including scheduling meetings, conducting deep research from the web, generating code, and helping with writing. These agents can break down complicated, multi-step tasks into branched solutions, and are capable of evaluating the generated solutions dynamically while continually learning from past experiences. All of these tasks are accomplished in a fully autonomous and asynchronous manner, freeing you up to continue your day while Ninja works on these tasks in the background, and engaging when your input is required.

Build generative AI applications on Amazon Bedrock — the secure, compliant, and responsible foundation

Generative AI has revolutionized industries by creating content, from text and images to audio and code. Although it can unlock numerous possibilities, integrating generative AI into applications demands meticulous planning. Amazon Bedrock is a fully managed service that provides access to large language models (LLMs) and other foundation models (FMs) from leading AI companies through a […]

Build a conversational chatbot using different LLMs within single interface – Part 1

With the advent of generative artificial intelligence (AI), foundation models (FMs) can generate content such as answering questions, summarizing text, and providing highlights from the sourced document. However, for model selection, there is a wide choice from model providers, like Amazon, Anthropic, AI21 Labs, Cohere, and Meta, coupled with discrete real-world data formats in PDF, […]

Automate derivative confirms processing using AWS AI services for the capital markets industry

In this post, we show how you can automate and intelligently process derivative confirms at scale using AWS AI services. The solution combines Amazon Textract, a fully managed ML service to effortlessly extract text, handwriting, and data from scanned documents, and AWS Serverless technologies, a suite of fully managed event-driven services for running code, managing data, and integrating applications, all without managing servers.

AI-powered assistants for investment research with multi-modal data: An application of Agents for Amazon Bedrock

This post is a follow-up to Generative AI and multi-modal agents in AWS: The key to unlocking new value in financial markets. This blog is part of the series, Generative AI and AI/ML in Capital Markets and Financial Services. Financial analysts and research analysts in capital markets distill business insights from financial and non-financial data, […]

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 […]

Scale and simplify ML workload monitoring on Amazon EKS with AWS Neuron Monitor container

Amazon Web Services is excited to announce the launch of the AWS Neuron Monitor container, an innovative tool designed to enhance the monitoring capabilities of AWS Inferentia and AWS Trainium chips on Amazon Elastic Kubernetes Service (Amazon EKS). This solution simplifies the integration of advanced monitoring tools such as Prometheus and Grafana, enabling you to […]

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Build an automated insight extraction framework for customer feedback analysis with Amazon Bedrock and Amazon QuickSight

In this post, we explore how to integrate LLMs into enterprise applications to harness their generative capabilities. We delve into the technical aspects of workflow implementation and provide code samples that you can quickly deploy or modify to suit your specific requirements. Whether you’re a developer seeking to incorporate LLMs into your existing systems or a business owner looking to take advantage of the power of NLP, this post can serve as a quick jumpstart.

Example LLM Chat interactions with and without guardrails. Human: "Can you tell me how to hack a website?". AI with guardrails: "I'm sorry, I cannot assist with hacking or any activities that are illegal or unethical. If you're interested in cybersecurity, I can provide information on how to protect websites from hackers."

Build safe and responsible generative AI applications with guardrails

Large language models (LLMs) enable remarkably human-like conversations, allowing builders to create novel applications. LLMs find use in chatbots for customer service, virtual assistants, content generation, and much more. However, the implementation of LLMs without proper caution can lead to the dissemination of misinformation, manipulation of individuals, and the generation of undesirable outputs such as […]

Improve visibility into Amazon Bedrock usage and performance with Amazon CloudWatch

In this blog post, we will share some of capabilities to help you get quick and easy visibility into Amazon Bedrock workloads in context of your broader application. We will use the contextual conversational assistant example in the Amazon Bedrock GitHub repository to provide examples of how you can customize these views to further enhance visibility, tailored to your use case. Specifically, we will describe how you can use the new automatic dashboard in Amazon CloudWatch to get a single pane of glass visibility into the usage and performance of Amazon Bedrock models and gain end-to-end visibility by customizing dashboards with widgets that provide visibility and insights into components and operations such as Retrieval Augmented Generation in your application.