Category: Thought Leadership

In April, Amazon Web Services (AWS) unveiled a suite of groundbreaking features for Amazon Bedrock and Amazon Q, ushering in a new era of generative AI capabilities. Learn how AWS Partners are leveraging the latest Amazon Bedrock and Amazon Q features to transform how they build, scale, and deploy intelligent applications—unlocking unprecedented opportunities for innovation and efficiency.

By Israel Mendes, Data Engineering leader – Caylent By Washim Nawaz, Analytics Specialist – AWS By Isaac Owusu-Hemeng, Customer Solutions Manager – AWS By Muz Syed, Sr. Partner Solutions Architect – AWS Caylent  In this post, we will discuss how APN Premier partner Caylent leveraged a 3-Day Experience Based Acceleration (EBA) workshop to finalize and […]

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.

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.

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.

Contact centers can improve customer experiences using generative AI, which creates new content and conversations. Accenture’s Connected Customer Experience (CCE) solution incorporates AWS services to provide personalized human and AI interactions. It uses generative AI for agent assist, call summarization, and self-service FAQs. By leveraging generative AI on AWS, CCE aims to enhance agent productivity, reduce handle times, and deliver exceptional customer experiences.

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.

Sustainability is a strategic priority for organizations in the global transition to net-zero emissions. Many AWS Partners have chosen to establish a Cloud Center of Excellence (CCoE) to achieve their decarbonization goals. Learn how to incorporate sustainability into your organization with a CCoE and how AWS services can help. With these efforts in place, your business can consider joining the 460 signatories who have signed The Climate Pledge and are working towards carbon neutrality by 2040.

Discover how a collaboration with AWS SaaS Factory revolutionized nClouds’ approach to SaaS and empowered its team to amplify its capabilities. Leveraging SaaS Factory’s technical knowledge equipped nClouds with the assurance and actionable insights needed to conquer the intricacies of the SaaS cloud landscape. For customers like OperationsRx, aligning with a SaaS Competency Partner like nClouds meant tapping into a reservoir of expertise, strengthened by years of experience from industry-leading experts.

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.