Tag: AWS Partner Guest Post

Amazon Athena allows analyzing petabytes of data directly with SQL queries or via analytics tools like ThoughtSpot. This post covers creating an IAM access key, two Amazon S3 buckets (one for data storage, one for query results), creating an Amazon Athena table from sample data using an AWS Glue crawler, and establishing a connection between Athena and ThoughtSpot. With the connection, ThoughtSpot can search and visualize the Athena data using AI capabilities.

In the transportation and logistics industry, navigating large vehicles comes with challenges like bridge heights, travel restrictions, and compliance. A previous AWS blog explored efficient truck routing using Amazon Location Service and HERE Technologies. This new post explores the HERE Explore Truck map styles to help visualize routes in real-time. By combining this style with Amazon Location Service, companies can leverage safe, efficient, and compliant routing for improved fleet operations.

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.

Instinet is a SaaS-based solution that Ness Digital Engineering built on AWS for the trading and finance industry. It provides technologically advanced brokerage services and helps institutional investors improve performance by reducing complexity and costs. The solution modernized Instinet’s trade processing and solved challenges with Instinet’s previous on-premises, batch-driven architecture.

Women’s sports have lacked access to data analytics geared towards enhancing their performance. AWS and KINEXON collaborated to develop player tracking technology tailored for women, with key innovations including personalized load management plans based on an athlete’s cycle. This helps establish meaningful performance thresholds derived from data, and build a supportive community to advance equality through sports technology specifically designed for women.

AWS HealthImaging is a new HIPAA-eligible service for storing, analyzing, and sharing medical imaging data securely in the cloud. CitiusTech developed a solution leveraging AWS services like HealthImaging to automate ingesting DICOM data. It scans for malware, validates DICOM files, copies clean images to HealthImaging for storage, and notifies users. Healthcare providers can easily migrate imaging workloads to realize improved accessibility and cost-efficiency.

SaaS relies on the cloud for efficient delivery, but faces data challenges like tenant isolation, high storage costs, and compliance requirements. Amazon FSx for NetApp ONTAP solves these by providing multi-tenancy, storage efficiencies, persistent Kubernetes storage, and more. It enables SaaS providers to enhance agility while managing data availability, protection, security and costs. Arcesium reduced dev/test refreshes by 80% and overall costs by 46% using FSx for ONTAP.

Pinecone has developed a novel serverless vector database architecture optimized for AI workloads like retrieval-augmented generation. Built on AWS, it decouples storage and compute and enables efficient intermittent querying of large datasets. This provides elasticity, fresher data, and major cost savings over traditional architectures. Pinecone serverless removes bottlenecks to building more knowledgeable AI applications economically at scale on AWS.

The travel and transport industry faces increasing pressure to reduce emissions due to environmental regulations and consumer demand. However, a gap exists between consumers’ stated willingness to pay more for sustainable options and their actual behavior. TCS GreenMiles Mobility aims to bridge this gap by providing data-driven insights, discoverability of emissions hotspots, control over reducing emissions through operational changes, and predictability through modeling net-zero pathways.

The Synopsys.ai electronic design automation (EDA) suite on AWS harnesses AI to optimize chip design. A key component is Synopsys DSO.ai, using reinforcement learning to enhance power, performance, and area. Benefits include faster optimization, better engineer productivity, design reuse, and faster process node migration. Deploying on AWS ParallelCluster provides auto-scaling, elasticity, and fast setup for massive EDA workloads. Testing showed 20% lower power, timing closure improvements, and significant cost savings.