AWS Partner Network (APN) Blog

Tag: Amazon SageMaker

Amplitude-AWS-Partners

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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Quantiphi-AWS-Partners

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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Capturing Crisis Communication Events in Real-Time Using Whispir and Amazon EventBridge

Work is becoming more complex, collaborative, and fast-paced in the time of COVID-19. With the role of crisis communications increasing in visibility, the capability of SaaS applications to facilitate actionable communications is paramount. Whispir’s integration of Amazon EventBridge means the Whispir platform can power communication workflows between your apps, systems, and databases without the need to write any connection code.

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How TIBCO Leverages AWS for its COVID-19 Analytics App

TIBCO Software has launched an analytics app to track the spread and impact of the COVID-19 pandemic in real-time, over local regions worldwide. The goal of this analytics app is to enable organizations to assess the potential impact of the COVID-19 pandemic on their business fabric, using sound data science and data management principles, in the context of real-time operations. Learn some of key capabilities of the app and how it was developed on AWS.

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Accenture-AWS-Partners

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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Next-Caller-AWS-Partners

Building a Data Processing and Training Pipeline with Amazon SageMaker

Next Caller uses machine learning on AWS to drive data analysis and the processing pipeline. Amazon SageMaker helps Next Caller understand call pathways through the telephone network, rendering analysis in approximately 125 milliseconds with the VeriCall analysis engine. VeriCall verifies that a phone call is coming from the physical device that owns the phone number, and flags spoofed calls and other suspicious interactions in real-time.

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Automation-1

Architecting Successful SaaS: Understanding Cloud-Based Software-as-a-Service Models

As the old saying goes, “You never get a second chance to make a first impression.” Customer trust is hard-earned and easily lost. Properly architecting a scalable and secure SaaS-based product is just as important as feature development and sales. No one wants to fail on Day 1— you worked too hard to get there. Get a comprehensive introduction to the common ways in which customers consume cloud-based SaaS models, and explore the different ways in which ISVs sell their software products to customers.

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Machine Learning-3

Accelerating Machine Learning with Qubole and Amazon SageMaker Integration

Data scientists creating enterprise machine learning models to process large volumes of data spend a significant portion of their time managing the infrastructure required to process the data, rather than exploring the data and building ML models. You can reduce this overhead by running Qubole data processing tools and Amazon SageMaker. An open data lake platform, Qubole automates the administration and management of your resources on AWS.

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Slalom-AWS-Partners

How Slalom and WordStream Used MLOps to Unify Machine Learning and DevOps on AWS 

Deploying AI solutions with ML models into production introduces new challenges. Machine Learning Operations (MLOps) has been evolving rapidly as the industry learns to marry new ML technologies and practices with incumbent software delivery systems and processes. WordStream is a SaaS company using ML capabilities to help small and mid-sized businesses get the most out of their online advertising. Learn how Slalom developed ML architecture to help WordStream productionize their machine learning efforts.

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Machine Learning-4

How to Use Amazon SageMaker to Improve Machine Learning Models for Data Analysis

Amazon SageMaker provides all the components needed for machine learning in a single toolset. This allows ML models to get to production faster with much less effort and at lower cost. Learn about the data modeling process used by BizCloud Experts and the results they achieved for Neiman Marcus. Amazon SageMaker was employed to help develop and train ML algorithms for recommendation, personalization, and forecasting models that Neiman Marcus uses for data analysis and customer insights.

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