AWS Partner Network (APN) Blog

Category: Amazon Machine Learning

Machine Learning-4

Using Fewer Resources to Run Deep Learning Inference on Intel FPGA Edge Devices

Inference is an important stage of machine learning pipelines that deliver insights to end users from trained neural network models. These models are deployed to perform predictive tasks like image classification, object detection, and semantic segmentation. However, constraints can make implementing inference at scale on edge devices such as IoT controllers and gateways challenging. Learn how to train and convert a neural network model for image classification to an edge-optimized binary for Intel FPGA hardware.

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How to Leverage APN Navigate to Prepare for the AWS Machine Learning Competency

Machine learning is a core component of tomorrow’s technology solutions. That’s why we are working with customers and APN Partners to drive this transformation journey. AWS has developed several partner programs to accelerate the process and enable you to create value for customers. This post explores APN Navigate, a comprehensive enablement program for APN Partners, and the AWS Machine Learning Competency, which validates and promotes a partner’s expertise in ML.

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How a Global Broadcaster Deployed Real-Time Automated News Clipping with AWS Media Services

As mobile devices and 5G networks pave the way for diversified content consumption across the globe, the new global media trend poses new challenges to traditional broadcasting companies. MegazoneCloud’s customer, a global broadcaster based in South Korea, turned to the cloud for help transforming its media production system and providing leading-edge services to its audience. The customer adopted state-of-the-art, cloud-based media technology, and undertook an industry-leading digital transformation.

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Measuring the Effectiveness of Personalization with Amplitude and Amazon Personalize

Companies attempting to deploy personalized customer experiences face many challenges. To do personalization well, you must understand the behavior of specific user segments and their affinities for specific products. However, you can’t uncover affinities and propensities without product analytics. Learn how to combine Amazon Personalize’s machine learning algorithms with Amplitude’s product intelligence platform to track user behavior in real-time.

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Improving Data Extraction Processes Using Amazon Textract and Idexcel

Manually extracting data from multiple sources is repetitive, error-prone, and can create a bottleneck in the business process. Idexcel built a solution based on Amazon Textract that improves the accuracy of the data extraction process, reduces processing time, and boosts productivity to increase operational efficiencies. Learn how this approach can solidify your competitive edge, help you respond faster to market opportunities, and increase operational efficiency.

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Cognitive Document Processing and Data Extraction for the Oil and Gas Industry

The oil and gas industry is highly complex and churns out copious amounts of data from sensors and machines at every stage in their business value chain. This post analyzes the role of machine learning for document extraction in the oil and gas industry for better business operations. Learn about Quantiphi’s document processing solution built on AWS, and how it helped a Canadian oil and gas organization address document management challenges through AI and ML techniques.

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How SF Medic Provides Real-Time Clinical Decision Support Using AWS Machine Learning Services

The healthcare industry is experiencing a global shortage of doctors, nurses, and other healthcare professionals. Telemedicine, which provides primary healthcare services to patients through remote connectivity, is one approach for addressing this challenge. SourceFuse developed an easy-to-use and secure telemedicine application called SF Medic that can be adopted by hospitals, clinics, and even single-physician practices.

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Optimizing Supply Chains Through Intelligent Revenue and Supply Chain (IRAS) Management

Fragmented supply-chain management systems can impair an enterprise’s ability to make informed, timely decisions. Accenture’s Intelligent Revenue and Supply Chain (IRAS) platform integrates insights generated by machine learning models into an enterprise’s technical and business ecosystems. This post explains how Accenture’s IRAS solution is architected, how it can coexist with other ML forecasting models or statistical packages, and how you can consume its insights in an integrated way.

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Training Multiple Machine Learning Models Simultaneously Using Spark and Apache Arrow

Spark is a distributed computing framework that added new features like Pandas UDF by using PyArrow. You can leverage Spark for distributed and advanced machine learning model lifecycle capabilities to build massive-scale products with a bunch of models in production. Learn how Perion Network implemented a model lifecycle capability to distribute the training and testing stages with few lines of PySpark code. This capability improved the performance and accuracy of Perion’s ML models.

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TCS-AWS-Partners

Intelligent Call Routing Using Amazon Fraud Detector and Amazon Connect

Amazon Fraud Detector is a fully managed service that makes it easy to identify potentially fraudulent online activities, such as online payment fraud and the creation of fake accounts. Learn how APN Premier Consulting Partner TCS has been integrating Amazon Fraud Detector to detect spam calls and route them efficiently using Amazon Connect. Used together, these AWS services can distinguish your genuine customers from spam or fraudulent callers.

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