AWS Machine Learning Blog
How Deltek uses HAQM Bedrock for question and answering on government solicitation documents
This post provides an overview of a custom solution developed by the AWS Generative AI Innovation Center (GenAIIC) for Deltek, a globally recognized standard for project-based businesses in both government contracting and professional services. Deltek serves over 30,000 clients with industry-specific software and information solutions. In this collaboration, the AWS GenAIIC team created a RAG-based solution for Deltek to enable Q&A on single and multiple government solicitation documents. The solution uses AWS services including HAQM Textract, HAQM OpenSearch Service, and HAQM Bedrock.
Cisco achieves 50% latency improvement using HAQM SageMaker Inference faster autoscaling feature
Webex by Cisco is a leading provider of cloud-based collaboration solutions which includes video meetings, calling, messaging, events, polling, asynchronous video and customer experience solutions like contact center and purpose-built collaboration devices. Webex’s focus on delivering inclusive collaboration experiences fuels our innovation, which leverages AI and Machine Learning, to remove the barriers of geography, language, personality, and familiarity with technology. Its solutions are underpinned with security and privacy by design. Webex works with the world’s leading business and productivity apps – including AWS. This blog post highlights how Cisco implemented faster autoscaling release reference.
How Cisco accelerated the use of generative AI with HAQM SageMaker Inference
This post highlights how Cisco implemented new functionalities and migrated existing workloads to HAQM SageMaker inference components for their industry-specific contact center use cases. By integrating generative AI, they can now analyze call transcripts to better understand customer pain points and improve agent productivity. Cisco has also implemented conversational AI experiences, including chatbots and virtual agents that can generate human-like responses, to automate personalized communications based on customer context. Additionally, they are using generative AI to extract key call drivers, optimize agent workflows, and gain deeper insights into customer sentiment. Cisco’s adoption of SageMaker Inference has enabled them to streamline their contact center operations and provide more satisfying, personalized interactions that address customer needs.
Discover insights from Box with the HAQM Q Box connector
Seamless access to content and insights is crucial for delivering exceptional customer experiences and driving successful business outcomes. Box, a leading cloud content management platform, serves as a central repository for diverse digital assets and documents in many organizations. An enterprise Box account typically contains a wealth of materials, including documents, presentations, knowledge articles, and […]
How Twilio generated SQL using Looker Modeling Language data with HAQM Bedrock
As one of the largest AWS customers, Twilio engages with data, artificial intelligence (AI), and machine learning (ML) services to run their daily workloads. This post highlights how Twilio enabled natural language-driven data exploration of business intelligence (BI) data with RAG and HAQM Bedrock.
Improve AI assistant response accuracy using Knowledge Bases for HAQM Bedrock and a reranking model
AI chatbots and virtual assistants have become increasingly popular in recent years thanks the breakthroughs of large language models (LLMs). Trained on a large volume of datasets, these models incorporate memory components in their architectural design, allowing them to understand and comprehend textual context. Most common use cases for chatbot assistants focus on a few […]
Automate the machine learning model approval process with HAQM SageMaker Model Registry and HAQM SageMaker Pipelines
This post illustrates how to use common architecture principles to transition from a manual monitoring process to one that is automated. You can use these principles and existing AWS services such as HAQM SageMaker Model Registry and HAQM SageMaker Pipelines to deliver innovative solutions to your customers while maintaining compliance for your ML workloads.
Build custom generative AI applications powered by HAQM Bedrock
With my blog post from June, I started a series that highlights the key factors that are driving customers to choose HAQM Bedrock. I explored how Bedrock enables customers to build a secure, compliant foundation for generative AI applications. Now I’d like to turn to a slightly more technical, but equally important differentiator for Bedrock—the multiple techniques that you can use to customize models and meet your specific business needs.
Use HAQM Bedrock to generate, evaluate, and understand code in your software development pipeline
Generative artificial intelligence (AI) models have opened up new possibilities for automating and enhancing software development workflows. Specifically, the emergent capability for generative models to produce code based on natural language prompts has opened many doors to how developers and DevOps professionals approach their work and improve their efficiency. In this post, we provide an […]
Inference AudioCraft MusicGen models using HAQM SageMaker
Music generation models have emerged as powerful tools that transform natural language text into musical compositions. Originating from advancements in artificial intelligence (AI) and deep learning, these models are designed to understand and translate descriptive text into coherent, aesthetically pleasing music. Their ability to democratize music production allows individuals without formal training to create high-quality […]