Category: Amazon SageMaker

In this blog, you’ll learn how Neurons Lab, an AWS Advanced Tier Services Partner, collaborated with Peak Defence to automate compliance processes using Amazon Bedrock with Anthropic Claude 3 model and Amazon Sagemaker. The generative AI solution streamlines cybersecurity audits and RFP responses, reducing time and resources required. It covers architectural considerations, operationalization with AWS services, LLM evaluation, and continuous improvement.

By Vanitha Jayasuriya, Full Stride Cloud Solution Lead, Germany – Wipro By Yedu Kuruvath, Alliances and Partner Development Lead, AI Practice – Wipro By Shaban Saddique, AWS Business Group Director, Europe – Wipro By Benson Philip, Sr Partner Development Manager, EMEA – AWS By Bindhu Chinnadurai, Sr Partner Solutions Architect, EMEA – AWS Wipro Introduction […]

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.

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 However, organizations face challenges in gathering and consolidating data from multiple, often outdated sources on the manufacturing floor.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.

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.

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.