Category: Artificial Intelligence

AWS embraces the “working backwards” approach to stay customer-focused. The Generative AI Center of Excellence (CoE) for AWS Partners applies this methodology and collects partner feedback to provide relevant insights, tools, and resources on leveraging generative AI. Recent updates to the CoE include customer research on generative AI adoption challenges, a usage maturity heatmap by industry, and five new use case deep dives covering telecom, automotive, IDP, contact centers, and financial analysts.

With the ability to automate tasks, enhance productivity, and enable hyper-personalized customer experiences, businesses are seeking specialized expertise to build a successful generative AI strategy. To support this need, we’re excited to announce the AWS Generative AI Competency—an AWS Specialization that helps AWS customers more quickly adopt generative AI solutions and strategically position themselves for the future.

KewSustain is an AI platform by KewMann built on AWS that addresses gaps in sustainability compliance. It efficiently gathers ESG data from multiple sources, analyzes it, and generates sustainability reports adhering to major frameworks. Key features include automated data collection, collaboration tools, recommendations based on various risk factors, real-time insights via dashboards, and a virtual assistant providing guidance on reporting requirements.

ServiceNow and AWS have collaborated to bridge language barriers in global workforces. Using AWS services like Amazon Translate and Amazon Comprehend, the AWS Translate Spoke for ServiceNow Flow Designer enables automatic translation of text into employees’ native languages. By demonstrating how the AWS Translate Spoke can translate knowledge articles, this post explains how ServiceNow customers can easily build multi-language workflows to serve global users.

Generating high-quality synthetic data protects privacy and augments scarce real-world data for training machine learning models. This post shows how to integrate the Gretel synthetic data platform with Amazon SageMaker Pipelines for a full ML workflow. Gretel’s integration with SageMaker Pipelines in a hybrid or fully managed cloud environment enables responsible and robust adoption of AI while optimizing model accuracy. With Gretel, data scientists can overcome data scarcity without compromising individuals’ privacy.

Companies often struggle to understand customer sentiment in call center data, posing barriers to responding to emotions in real-time. SuccessKPI uses natural language understanding and machine learning on a large dataset to enable sentiment prediction. This helps avoid bias of human reviewers and attain objective results when analyzing customer feedback. SuccessKPI offers capabilities like sentiment by channel, time, quarter, and entity to transform customer experiences.

Marriott’s CISO Arno Van Der Walt manages cybersecurity through a “human-centered, data-driven, technology-enabled” approach aimed at making security frictionless. Critical partnerships with AWS, Deloitte, and Palo Alto Networks leverage AI/ML to share threat data and empower “impossible” autonomous security. Together, their tri-party services provide an end-to-end platform unifying business and security data to detect threats and enable quick response.

Intelligent document processing (IDP) automates data extraction from diverse document formats, accelerating information retrieval. Manually labeling is expensive and difficult, and Cognizant’s IDP solution on AWS automates document labeling at scale to overcome this challenge. Its customized user interface in Amazon SageMaker Ground Truth lets subject matter experts efficiently label documents.

Large language models like Amazon Bedrock can generate MongoDB queries from natural language questions, transforming how users access NoSQL databases. By leveraging AI and language models, this solution allows business users to query MongoDB data through conversational English instead of code. It connects to MongoDB with PyMongo, generates queries with LangChain and Bedrock, retrieves and formats results into natural language answers.

MLOps applies DevOps principles to machine learning, enabling organizations to efficiently develop, deploy, and manage models at scale. Eviden’s 10-step MLOps assessment examines existing models, establishes governance, creates self-service access, scales data analysis, registers models, enables feature re-use, provides data access, tests models at scale, deploys models, and enables API access. This end-to-end approach streamlines model creation and deployment while ensuring governance and consistency.