AWS Partner Network (APN) Blog

Tag: APN References

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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APN TV-1

New APN TV Series Showcases How AWS Competency Partners Help Customers Grow with AWS

The Next Smart video series on APN TV showcases how AWS Competency Partners are helping customers grow with AWS. Whether you’re looking for consulting services or strategic technology solutions, you’ll discover APN TV videos that show how AWS customers in similar situations have teamed up with AWS Competency Partners to drive better business and bigger results. The Next Smart video series on APN TV includes demos, interviews, success stories, and webinars featuring AWS Competency Partners.

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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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Mactores-AWS-Partners

Lower TCO and Increase Query Performance by Running Hive on Spark in Amazon EMR

Learn how Mactores helped Seagate Technology to use Apache Hive on Apache Spark for queries larger than 10TB, combined with the use of transient Amazon EMR clusters leveraging Amazon EC2 Spot Instances. It was imperative for Seagate to have systems in place to ensure the cost of collecting, storing, and processing data did not exceed their ROI. Moving to Hive on Spark enabled Seagate to continue processing petabytes of data at scale with significantly lower TCO.

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How CloudHedge Transforms Legacy Web Applications on Windows to Containerized Cloud-Native on AWS

Many vendors, services, and resources are available to migrate one or two applications at a time, but most struggle to migrate a large number of applications with the same skill level and outcomes. A tool that provided effective application discovery and containerization for a large number of apps was missing. CloudHedge’s automated discovery, containerization, and deployment tools can reduce the time and effort required to transform large numbers of monolithic, on-premises Windows applications to cloud-native services running on AWS.

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Mactores-AWS-Partners

Optimizing Presto SQL on Amazon EMR to Deliver Faster Query Processing

Seagate asked Mactores Cognition to evaluate and deliver an alternative data platform to process petabytes of data with consistent performance. It needed to lower query processing time and total cost of ownership, and provide the scalability required to support about 2,000 daily users. Learn about the the three migration options Mactores tested and the architecture of the solution Seagate selected. This effort improved the overall efficiency of Seagate’s Amazon EMR cluster and business operations.

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Capgemini-AWS-Partners

Developing Payment Card Industry Compliant Solutions on AWS to Protect Customer Data

Financial institutions possess and process data that are very sensitive and have immense business value. In recent years, regulations like open banking and data residency law have forced organizations to be even more adaptive to frequent challenges to systems storing and processing the data. Explore how Capgemini developed an application to address this customer challenge and learn how the approach helped worldwide credit card provider comply with PCI DSS security standards.

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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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Cazena-AWS-Partners

Accelerating Apache and Hadoop Migrations with Cazena’s Data Lake as a Service on AWS

Running Hadoop, Spark, and related technologies in the cloud provides the flexibility required by these distributed systems. Cazena provides a production-ready, continuously optimized and secured Data Lake as a Service with multiple features that enables migration of Hadoop and Spark analytics workloads to AWS without the need for specialized skills. Learn how Cazena makes it easy to migrate to AWS while ensuring your data is as secure on the cloud as it is on-premises.

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How Steamhaus Used AWS Well-Architected to Improve Sperry Rail’s Artificial Intelligence System

Over two days, Steamhaus conducted an AWS Well-Architected Review on-site with the team who designed, built, and currently manage Elmer at Sperry Rail. Elmer uses machine intelligence to inspect thousands of miles of ultrasound scans collected by Sperry’s inspection vehicles, searching for evidence of cracks in the rail. This partnership allowed quick improvements in efficiency, while ensuring the requirements of running the business day-to-day did not get in the way of improving Elmer.

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