AWS Partner Network (APN) Blog

Category: Amazon Machine Learning

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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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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Boosting the Assembly and Deployment of Artificial Intelligence Solutions with KNIME Visual Data Science Tools

With rapid advancements in machine learning techniques over the past decade, intelligent decision-making and prediction systems are poised to transform productivity and lead to significant economic gains. KNIME provides visual data science tools to help data science teams rapidly build and deploy data-driven solutions that integrate with AWS decision support tools and services. Learn about the barriers to adoption of AI and the ways in which the KNIME tools remove those barriers.

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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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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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How Slalom Uses AWS DeepRacer to Upskill its Workforce in Reinforcement Learning

AWS DeepRacer allows developers of all skill levels to get started with reinforcement learning, which is an advanced machine learning technique that learns very complex behaviors without requiring any labeled training data, and can make short-term decisions while optimizing for a longer term goal. Learn how Slalom created AWS DeepRacer experiences for its own workforce. The cars and tracks now regularly appear in at Slalom locations across the world as valuable internal learning events.

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How to Orchestrate a Data Pipeline on AWS with Control-M from BMC Software

In spite of the rich set of machine learning tools AWS provides, coordinating and monitoring workflows across an ML pipeline remains a complex task. Control-M by BMC Software that simplifies complex application, data, and file transfer workflows, whether on-premises, on the AWS Cloud, or across a hybrid cloud model. Walk through the architecture of a predictive maintenance system we developed to simplify the complex orchestration steps in a machine learning pipeline used to reduce downtime and costs for a trucking company.

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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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