AWS Partner Network (APN) Blog

Tag: Machine Learning

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How Blue People Detected Application Anomalies Using Insights from Amazon DevOps Guru 

Amazon DevOps Guru is a machine learning-powered service that detects abnormal application behavior and provides insights about the anomalous behavior. These insights are supported with metrics and events related to the anomaly and recommendations to help address and mitigate the anomalous behavior. Learn how Blue People used insights to identify the root cause for a non-responsive application that was otherwise hard to detect.

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Engage360 is an Amazon Kendra-Powered App to Optimize Search and Recommendation Experience in Salesforce CRM

Engage360, built by Persistent Systems and powered by Amazon Kendra, is a security-certified app on Salesforce AppExchange that lets you provide machine learning-powered search and recommendations right inside Salesforce Sales Cloud, Salesforce Service Cloud, and Salesforce Financial Services Cloud. It transforms how Salesforce users securely discover, access, and deliver relevant knowledge distributed across disparate enterprise information silos and content formats.

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How to Accelerate SAP Insights with Qlik, Snowflake, and AWS

Sales order fulfillment and billing can impact customer satisfaction, and receivables and payments affect working capital and cash liquidity. As a result, the order-to-cash process is the lifeblood of the business and is critical to optimize. Qlik Cloud Data Integration accelerators integrate with Snowflake to automate the ingestion, transformation, and analytics to solve some of the most common SAP business problems, enabling users to derive business insights that can drive decision-making.

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Powering Business Process Automation with Machine Learning Using Pega and Amazon SageMaker

Through the Pega Platform and Amazon SageMaker, you can easily streamline the development and operationalization of machine learning models to improve process automation. This allows customers to combine the strengths of cloud, data, and machine learning with AI-powered decisioning and smart workflow capabilities. It also enables customers to operationalize and monetize data and insight, drive process efficiency and effectiveness, and improve customer experience and value.

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How Ganit Helps Customers Optimize Their Inventory by Leveraging Amazon Forecast

Predicting demand for medical products can be a formidable challenge, since many items have no underlying seasonality patterns nor a consistent shelf life. Learn how Ganit worked with a client to achieve reductions in inventory by designing a robust solution with Amazon Forecast. This post details the approach used to define the objectives and discover the data treatments, and cover employing the flexible architecture provided by Forecast to turn the client’s data into a strength.

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What Do Consumers Really Think of Automated Customer Service?

Conversational AI solutions, like chatbots and interactive voice response systems (IVR), are a key component of enterprises’ customer service strategy. AWS recently ran a survey, through ESG, on consumers’ opinions of automated customer service solutions like chatbots and IVRs. Conversational AI solutions have come a long way from basic FAQ experiences, and while we see strong positive signals of consumer interest in automated solutions, there are still areas for improvement.

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Privacy-Preserving Federated Learning on AWS with NVIDIA FLARE

Federated learning (FL) addresses the need of preserving privacy while having access to large datasets for machine learning model training. The NVIDIA FLARE (which stands for Federated Learning Application Runtime Environment) platform provides an open-source Python SDK for collaborative computation and offers privacy-preserving FL workflows at scale. NVIDIA is an AWS Competency Partner that has pioneered accelerated computing to tackle challenges in AI and computer graphics.

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Explore Key Themes in the AWS Machine Learning Visionaries Partners Report

The AWS Machine Learning Visionaries Partners Report is a quarterly series that tracks, selects, collates, and distributes horizontal technology capabilities enabled by machine learning in areas that AWS expects to be transformative in 1-3 years. The series’ purpose is to share our insights with AWS Partners and to collect their interest, expertise, and insights in co-building along these prioritized themes. The reports include updates on series topics as we see changes in those areas, and new topics will also be added.

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Fast, Accurate, Alternate Credit Decisioning Using ElectrifAi’s Machine Learning Solution on AWS

Infusing machine learning into core business processes such as credit scoring creates a competitive edge for banks and financial services institutions. It does not require a data science team, expertise, or platform rollout. Explore an ML-based credit-decisioning model built by ElectrifAi in collaboration with AWS whose model rapidly determines the creditworthiness of a SME, and data-driven, actionable insights reduce the overall processing cost and are consistent and free from any potential human biases.

Presidio Builds Conversational Bots Using Amazon Lex and the Amazon Chime SDK

With the rise of voice assistants like Amazon Alexa, customer expectations for handling inquiries and transactions have shifted from the outdated phone keypad, also known as dual tone multi-frequency (DTMF), to modern conversational AI that enables machines to communicate with human beings. In this post, we demonstrate how Presidio implemented conversational AI to check the wait time and reserve a table at a restaurant using Amazon Chime SDK, Amazon Lex, and Amazon Polly.