Category: Artificial Intelligence

Organizations can enhance resilience by implementing Amazon Security Lake for centralized security data storage, Splunk for real-time data analysis, and Recorded Future for advanced threat intelligence. This unified approach tackles data silos, complex analysis, slow threat detection, compliance challenges, and inefficient resource utilization. Security Lake aggregates data sources, Splunk analyzes it with AI/ML for swift threat identification, and Recorded Future provides external intelligence context.

Arcanum AI migrated its generative AI workloads from OpenAI to AWS using a two-phase model evaluation process. Open-source LLMs were tested out-of-the-box and with customized prompts, scored by experts, and evaluated against existing use cases. Amazon Bedrock provided a private network and access control for handling sensitive client data. AWS’s AI services enabled Arcanum to deploy top-performing LLMs securely in clients’ VPCs, outperforming OpenAI models while meeting security needs.

Organizations face increasing demands for transparency around environmental, social, and governance (ESG) matters. PwC’s Unified Sustainability Hub helps access and manage ESG data, providing automated data ingestion, a centralized view of sustainability performance, intelligent data extraction, carbon accounting, reporting/analytics, forecasting capabilities, and a generative AI assistant. It accelerates sustainability transitions by integrating stakeholders, processes, and standards.

TextRay is an information extraction solution that automatically extracts data from scanned documents using deep learning models. It leverages AWS services to provide a scalable and cost-effective way to process documents while reducing errors and turnaround time. An AWS CloudFormation template simplifies deployment, while a pre-trained base model demonstrates TextRay’s precision in extracting tabular and form data into structured CSV outputs.

Amazon Q and generative BI are transforming business operations, and AWS Partners like ZS Associates, Tiger Analytics, and Compass UOL are pioneering use cases leveraging these technologies to build industry-tailored solutions that improve decision making, operations, fraud detection, software development lifecycles, and more. AWS provides resources to help partners develop and deploy such transformative generative AI offerings.

NextBillion.ai provides mapping solutions for enterprises, aiming to deliver precise estimated time of arrival (ETA). It developed ENZYME, a system leveraging AWS services like Amazon SageMaker to evaluate map quality and improve ETA accuracy through machine learning. By feeding industry data into custom models, ENZYME reduces the mean absolute percentage error between estimated and actual arrival times by 10-20% compared to regular maps.

Generative AI adoption is rapidly growing to meet the massive data needs of modern machine learning models. Manually labeling data can be time-consuming but AWS has collaborated with Datasaur to offer solutions addressing data labeling challenges using generative AI. Datasaur’s NLP Platform automates annotation tasks and integrates with AWS services, and its LLM Labs evaluates large language models’ performance and cost for labeling.

In today’s industrial landscape, smart manufacturing is pivotal for sustainability and efficiency gains. However, organizations face challenges in gathering and consolidating data from multiple, often outdated sources on the manufacturing floor. LTIMindtree’s AWS-powered Digital Command Center (DCC) solution addresses this by enabling data acquisition, management, real-time KPI monitoring, and analytics-driven insights.

Contact centers often face challenges due to lack of visibility into customers’ omnichannel experiences. Joulica’s Customer Journey Analytics solution, part of AWS Contact Center Intelligence, provides a unified, real-time view of each customer’s journey across voice, digital, and social interactions. Built on AWS data streaming architecture, it empowers agents with holistic customer understanding and enhances customer satisfaction and brand perception through optimized experiences.

Fine-tuning large language models (LLMs) is crucial for leveraging their full potential across industries. Quantiphi unveils how fine-tuning supercharges LLMs to deliver domain-specific AI solutions that redefine possibilities. From personalized healthcare to precise financial predictions and streamlined legal reviews, fine-tuned models offer transformative value and unleash the power of customized, efficient, and responsible generative AI deployments.