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Amazon Redshift introduces reusable templates for COPY operations

Amazon Redshift now supports templates for the COPY command, allowing you to store and reuse frequently used COPY parameters. This new feature enables you to create reusable templates that contain commonly utilized formatting parameters, eliminating the need to manually specify parameters for each COPY operation.

Templates help maintain consistency across data ingestion operations that use the COPY command. They also reduce the time and effort required to execute COPY commands. You can create standardized configurations for different file types and data sources, ensuring consistent parameter usage across your teams and reducing the likelihood of errors caused by manual input. When parameters need to be updated, changes to the template automatically apply to all future uses, simplifying maintenance and improving operational efficiency.

Support for templates for the COPY command is available in all AWS Regions, including the AWS GovCloud (US) Regions, where Amazon Redshift is available. To get started with templates, see the documentation or check out the AWS Blog.

 

​Amazon Redshift now supports templates for the COPY command, allowing you to store and reuse frequently used COPY parameters. This new feature enables you to create reusable templates that contain commonly utilized formatting parameters, eliminating the need to manually specify parameters for each COPY operation. Templates help maintain consistency across data ingestion operations that use the COPY command. They also reduce the time and effort required to execute COPY commands. You can create standardized configurations for different file types and data sources, ensuring consistent parameter usage across your teams and reducing the likelihood of errors caused by manual input. When parameters need to be updated, changes to the template automatically apply to all future uses, simplifying maintenance and improving operational efficiency. Support for templates for the COPY command is available in all AWS Regions, including the AWS GovCloud (US) Regions, where Amazon Redshift is available. To get started with templates, see the documentation or check out the AWS Blog.  

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Amazon SageMaker Unified Studio adds light mode support for IAM-based domains

Today, AWS announces light mode support in Amazon SageMaker Unified Studio for IAM-based domains. Customers can now configure the visual interface mode to match their preference, choosing between dark and light themes.

Light mode helps improve readability in bright environments and provides a familiar visual experience for customers who prefer lighter interfaces. Combined with the existing dark mode, this update gives you full control over your development environment’s appearance, improving accessibility and reducing eye strain across varying lighting conditions. In SageMaker Unified Studio settings, you can click on ‘customize appearance’ under your Profile settings to choose between visual modes including dark and light. The setting persists across browsers and devices.

This feature is available in all regions where Amazon SageMaker Unified Studio is available. To learn more, refer to the User Guide.

 

​Today, AWS announces light mode support in Amazon SageMaker Unified Studio for IAM-based domains. Customers can now configure the visual interface mode to match their preference, choosing between dark and light themes.
Light mode helps improve readability in bright environments and provides a familiar visual experience for customers who prefer lighter interfaces. Combined with the existing dark mode, this update gives you full control over your development environment’s appearance, improving accessibility and reducing eye strain across varying lighting conditions. In SageMaker Unified Studio settings, you can click on ‘customize appearance’ under your Profile settings to choose between visual modes including dark and light. The setting persists across browsers and devices.
This feature is available in all regions where Amazon SageMaker Unified Studio is available. To learn more, refer to the User Guide.  

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Amazon Redshift introduces new array functions for semi-structured data processing

Amazon Redshift now supports nine new array functions for working with semi-structured data stored in the SUPER data type. The new functions include ARRAY_CONTAINS, ARRAY_DISTINCT, ARRAY_EXCEPT, ARRAY_INTERSECTION, ARRAY_POSITION, ARRAY_POSITIONS, ARRAY_SORT, ARRAY_UNION, and ARRAYS_OVERLAP, enabling you to search, compare, sort, and transform arrays directly within your SQL queries. Previously, performing these operations required writing complex custom PartiQL SQL logic.

These functions simplify complex data transformations and reduce query complexity by enabling sophisticated array operations in a single SQL statement. For example, you can use ARRAY_CONTAINS and ARRAY_POSITION for element lookup, ARRAY_INTERSECTION and ARRAY_EXCEPT for set operations, or ARRAY_SORT and ARRAY_DISTINCT to organize and deduplicate data. These functions are particularly valuable for applications involving nested data structures, event processing, and analytics workflows where data needs to be aggregated, filtered, or transformed at scale.

The new Amazon Redshift array functions are available in all AWS Regions, including the AWS GovCloud (US) Regions, where Amazon Redshift is available. To learn more, please visit our documentation.

 

​Amazon Redshift now supports nine new array functions for working with semi-structured data stored in the SUPER data type. The new functions include ARRAY_CONTAINS, ARRAY_DISTINCT, ARRAY_EXCEPT, ARRAY_INTERSECTION, ARRAY_POSITION, ARRAY_POSITIONS, ARRAY_SORT, ARRAY_UNION, and ARRAYS_OVERLAP, enabling you to search, compare, sort, and transform arrays directly within your SQL queries. Previously, performing these operations required writing complex custom PartiQL SQL logic. These functions simplify complex data transformations and reduce query complexity by enabling sophisticated array operations in a single SQL statement. For example, you can use ARRAY_CONTAINS and ARRAY_POSITION for element lookup, ARRAY_INTERSECTION and ARRAY_EXCEPT for set operations, or ARRAY_SORT and ARRAY_DISTINCT to organize and deduplicate data. These functions are particularly valuable for applications involving nested data structures, event processing, and analytics workflows where data needs to be aggregated, filtered, or transformed at scale. The new Amazon Redshift array functions are available in all AWS Regions, including the AWS GovCloud (US) Regions, where Amazon Redshift is available. To learn more, please visit our documentation.  

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Amazon EC2 R8g instances now available in additional regions

Starting today, Amazon Elastic Compute Cloud (Amazon EC2) R8g instances are available in AWS Middle East (UAE), AWS Mexico (Central), and AWS Europe (Zurich) regions. These instances are powered by AWS Graviton4 processors and deliver up to 30% better performance compared to AWS Graviton3-based instances. Amazon EC2 R8g instances are ideal for memory-intensive workloads such as databases, in-memory caches, and real-time big data analytics. These instances are built on the AWS Nitro System, which offloads CPU virtualization, storage, and networking functions to dedicated hardware and software to enhance the performance and security of your workloads.

AWS Graviton4-based Amazon EC2 instances deliver the best performance and energy efficiency for a broad range of workloads running on Amazon EC2. AWS Graviton4-based R8g instances offer larger instance sizes with up to 3x more vCPU (up to 48xlarge) and memory (up to 1.5TB) than Graviton3-based R7g instances. These instances are up to 30% faster for web applications, 40% faster for databases, and 45% faster for large Java applications compared to AWS Graviton3-based R7g instances. R8g instances are available in 12 different instance sizes, including two bare metal sizes. They offer up to 50 Gbps enhanced networking bandwidth and up to 40 Gbps of bandwidth to the Amazon Elastic Block Store (Amazon EBS).

To learn more, see Amazon EC2 R8g Instances. To explore how to migrate your workloads to Graviton-based instances, see AWS Graviton Fast Start program and Porting Advisor for Graviton. To get started, see the AWS Management Console.

 

​Starting today, Amazon Elastic Compute Cloud (Amazon EC2) R8g instances are available in AWS Middle East (UAE), AWS Mexico (Central), and AWS Europe (Zurich) regions. These instances are powered by AWS Graviton4 processors and deliver up to 30% better performance compared to AWS Graviton3-based instances. Amazon EC2 R8g instances are ideal for memory-intensive workloads such as databases, in-memory caches, and real-time big data analytics. These instances are built on the AWS Nitro System, which offloads CPU virtualization, storage, and networking functions to dedicated hardware and software to enhance the performance and security of your workloads. AWS Graviton4-based Amazon EC2 instances deliver the best performance and energy efficiency for a broad range of workloads running on Amazon EC2. AWS Graviton4-based R8g instances offer larger instance sizes with up to 3x more vCPU (up to 48xlarge) and memory (up to 1.5TB) than Graviton3-based R7g instances. These instances are up to 30% faster for web applications, 40% faster for databases, and 45% faster for large Java applications compared to AWS Graviton3-based R7g instances. R8g instances are available in 12 different instance sizes, including two bare metal sizes. They offer up to 50 Gbps enhanced networking bandwidth and up to 40 Gbps of bandwidth to the Amazon Elastic Block Store (Amazon EBS). To learn more, see Amazon EC2 R8g Instances. To explore how to migrate your workloads to Graviton-based instances, see AWS Graviton Fast Start program and Porting Advisor for Graviton. To get started, see the AWS Management Console.  

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Amazon Redshift Serverless now maintains datashare permissions during restore

Amazon Redshift Serverless now preserves datashare permissions when you restore a snapshot to the same namespace, simplifying data sharing workflows and reducing administrative overhead. Previously, restoring a serverless namespace from a snapshot required administrators to manually re-grant datashare permissions to consumer clusters and recreate consumer databases, even when restoring to the same namespace.

With this enhancement, datashare permissions are automatically maintained when you restore a snapshot to the same producer namespace, provided the datashare permission existed both when the snapshot was taken and on the current namespace. For consumer namespaces, datashare access remains unchanged after restore, eliminating the need for producer administrators to re-grant permissions. This streamlines disaster recovery and testing workflows by reducing manual configuration steps and potential errors. Amazon Redshift also provides EventBridge notifications to alert you when datashares are dropped, consumer access is revoked, or public accessibility changes during restore operations.

This feature is available in all AWS Regions that support Amazon Redshift. To learn more, see the Amazon Redshift Management Guide.

 

​Amazon Redshift Serverless now preserves datashare permissions when you restore a snapshot to the same namespace, simplifying data sharing workflows and reducing administrative overhead. Previously, restoring a serverless namespace from a snapshot required administrators to manually re-grant datashare permissions to consumer clusters and recreate consumer databases, even when restoring to the same namespace.
With this enhancement, datashare permissions are automatically maintained when you restore a snapshot to the same producer namespace, provided the datashare permission existed both when the snapshot was taken and on the current namespace. For consumer namespaces, datashare access remains unchanged after restore, eliminating the need for producer administrators to re-grant permissions. This streamlines disaster recovery and testing workflows by reducing manual configuration steps and potential errors. Amazon Redshift also provides EventBridge notifications to alert you when datashares are dropped, consumer access is revoked, or public accessibility changes during restore operations. This feature is available in all AWS Regions that support Amazon Redshift. To learn more, see the Amazon Redshift Management Guide.  

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Explorar qué significa la IA para la educación y la próxima generación

Explorar qué significa la IA para la educación y la próxima generación

Graduado eleva su diploma hacia la luz del atardecer

Por: Paul Nyhan, escritor de Microsoft.

A medida que la IA transforma la manera en que trabajamos, vivimos y aprendemos, la educación superior es más que otro actor: debe liderar el camino.

La educación superior debe encontrar un equilibrio mientras prepara a la próxima generación para un mundo que se transforma con la IA. Debe enseñar a los estudiantes nuevas y necesarias habilidades de IA, manteniéndose fiel a la misión que la ha guiado durante siglos: cultivar el pensamiento crítico, la comunicación y los valores humanos.

Y tiene que hacer todo esto más rápido.

Estos son mensajes centrales de un nuevo libro, «Grados de cambio: qué significa la IA para la educación y la próxima generación», de Juan M. Lavista Ferres, quien dirige el Instituto de la Economía de la IA de Microsoft y es director del AI for Good Lab de la compañía.

«Primero, debemos dotar a las personas de las habilidades necesarias para usar la IA de forma eficaz — un paso fundamental para garantizar su difusión generalizada», escribe Lavista Ferres. «Segundo, y de manera más profunda, debemos entender cómo la propia IA transforma la educación en su conjunto.»

Aquí hay cuatro formas en que Lavista Ferres y los colaboradores del libro —decenas de autores y académicos— dicen que la educación superior puede y debe liderar en la era de la IA:

La alfabetización en IA no es una optativa; ahora es un requisito básico

La IA se vuelve, muy rápido, omnipresente, para ayudar a la gente con todo, desde pagar facturas hasta viajar en autobuses públicos. Eso significa que las universidades y colegios deben enseñar a los estudiantes habilidades técnicas de IA porque la alfabetización en IA, y en última instancia la fluidez en IA, serán necesarias tanto para el trabajo como para la vida personal. El mercado laboral ya lo ha comenzado a dejar claro. Los primeros datos muestran que la competencia en IA ya tiene un aumento salarial del 23%.

Este aprendizaje no puede ocurrir en el vacío. La alfabetización en IA debe integrarse en la enseñanza en instituciones y planes de estudio que se mantengan fundamentados en las competencias de la educación superior, incluidos el razonamiento ético, la descomposición y análisis de problemas, la comunicación y la colaboración. Aquí, los instructores están en primera línea de la economía de la IA.

La educación superior debe adaptarse con rapidez y mantenerse fiel a su misión

A medida que la IA acelera las tareas rutinarias y transforma la manera en que las personas resuelven problemas, la agencia humana debe permanecer en el centro de la educación superior. Los educadores deben integrar la IA generativa de forma reflexiva en cursos, formaciones, títulos e infraestructuras para apoyar este aprendizaje sin erosionar la misión de larga trayectoria de las instituciones.

Por igual importante, las escuelas deben avanzar más rápido, incluso a través de la integración de habilidades en IA y alfabetización ética en los planes de estudio, para hacer que la educación integral en IA sea accesible para todos los estudiantes.

La alfabetización es bidireccional

Los desarrolladores deben comprender el impacto de la IA en la sociedad y las preocupaciones éticas, mientras que educadores, responsables políticos y el público deben aprender cómo funciona la IA. El éxito depende de esta alfabetización dual, en la que cada grupo entiende el trabajo del otro.

La recompensa será enorme y el precio por la inacción será muy alto. Los trabajadores que externalizan el trabajo rutinario a la IA para poder centrarse en la supervisión, el trabajo creativo y los juicios complejos liderarán la innovación responsable. Pero sin alfabetización dual, la regulación y la adopción se retrasarán y dificultarán el progreso.

Los profesores y decanos no pueden hacer esto solos

El éxito en la era de la IA depende de que las universidades, la industria y los responsables políticos trabajen juntos. En medio de este trabajo en equipo, el profesorado y las instituciones deben desempeñar un papel principal para apoyar a los estudiantes que están a la vanguardia de la era de la IA. Pueden lograrlo a través de equilibrar la necesidad de agilidad académica con la misión de preparar de manera amplia a los graduados para el presente y el futuro.

Esto significa que las instituciones de educación superior deben establecer estándares claros para las credenciales técnicas de IA que sean reconocidos por los empleadores, proporcionar el apoyo financiero y estructural necesario para integrar las habilidades y la preparación en IA en los títulos y cursos, y garantizar la inclusión y accesibilidad para estudiantes diversos.

«Grados de cambio: Lo que significa la IA para la educación y la próxima generación» ya está disponible en Wiley y librerías online.

The post Explorar qué significa la IA para la educación y la próxima generación appeared first on Source LATAM.

 

​The post Explorar qué significa la IA para la educación y la próxima generación appeared first on Source LATAM.  

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Amazon EC2 I8ge instances now generally available in Europe (Ireland) AWS region.

Amazon Web Services (AWS) announces the availability of Amazon EC2 I8ge instances in Europe (Ireland) AWS region. Designed for large storage I/O intensive workloads, these new instances are powered by 5th generation Intel Xeon Scalable processors with an all-core turbo frequency of 3.2 GHz, offering up to 40% better compute performance and 20% better price performance over existing I3en instances.

I8ge instances offer up to 120TB local NVMe storage density—the highest available in the cloud for storage optimized instances—and deliver up to twice as many vCPUs and memory compared to prior generation instances. Powered by 3rd generation AWS Nitro SSDs, these instances achieve up to 65% better real-time storage performance, up to 50% lower storage I/O latency, and 65% lower storage I/O latency variability compared to I3en instances. Additionally, the 16KB torn write prevention feature, enables customers to eliminate performance bottlenecks for database workloads.

I8ge instances are high-density storage-optimized instances, for workloads that demand rapid local storage with high random read/write performance and consistently low latency for accessing large data sets. These versatile instances are offered in eleven different sizes including 2 metal sizes, providing flexibility to match customers computational needs. They deliver up to 180 Gbps of network performance bandwidth, and 60 Gbps of dedicated bandwidth for Amazon Elastic Block Store (EBS), ensuring fast and efficient data transfer for the most demanding applications.

To begin your Graviton journey, visit the Level up your compute with AWS Graviton page. To get started, see AWS Management Console, AWS Command Line Interface (AWS CLI), and AWS SDKs. To learn more, visit the I8ge instances page.

 

​Amazon Web Services (AWS) announces the availability of Amazon EC2 I8ge instances in Europe (Ireland) AWS region. Designed for large storage I/O intensive workloads, these new instances are powered by 5th generation Intel Xeon Scalable processors with an all-core turbo frequency of 3.2 GHz, offering up to 40% better compute performance and 20% better price performance over existing I3en instances. I8ge instances offer up to 120TB local NVMe storage density—the highest available in the cloud for storage optimized instances—and deliver up to twice as many vCPUs and memory compared to prior generation instances. Powered by 3rd generation AWS Nitro SSDs, these instances achieve up to 65% better real-time storage performance, up to 50% lower storage I/O latency, and 65% lower storage I/O latency variability compared to I3en instances. Additionally, the 16KB torn write prevention feature, enables customers to eliminate performance bottlenecks for database workloads. I8ge instances are high-density storage-optimized instances, for workloads that demand rapid local storage with high random read/write performance and consistently low latency for accessing large data sets. These versatile instances are offered in eleven different sizes including 2 metal sizes, providing flexibility to match customers computational needs. They deliver up to 180 Gbps of network performance bandwidth, and 60 Gbps of dedicated bandwidth for Amazon Elastic Block Store (EBS), ensuring fast and efficient data transfer for the most demanding applications. To begin your Graviton journey, visit the Level up your compute with AWS Graviton page. To get started, see AWS Management Console, AWS Command Line Interface (AWS CLI), and AWS SDKs. To learn more, visit the I8ge instances page.  

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Database Savings Plans now supports Amazon OpenSearch Service and Amazon Neptune Analytics

Today, AWS announces expanded coverage for Database Savings Plans, with support for Amazon OpenSearch Service and Amazon Neptune Analytics.

With Database Savings Plans, you can save up to 35% in exchange for a commitment to a consistent amount of usage (measured in $/hour) over a one-year term with no upfront payment. Database Savings Plans automatically applies to eligible serverless and provisioned instance usage regardless of supported engine, instance family, size, deployment option, or AWS Region. For example, with Database Savings Plans, you can change from m7i.large.search to c8g.2xlarge.search within OpenSearch Service, or scale Neptune Analytics workloads while continuing to benefit from the discounted pricing.

Database Savings Plans for Amazon OpenSearch Service and Amazon Neptune Analytics is available starting today in all AWS Regions, except China Regions.

You can get started with Database Savings Plans from the AWS Billing and Cost Management Console or by using the AWS CLI. To realize the largest savings, you can make a commitment to Savings Plans by using purchase recommendations provided in the console. For a more customized analysis, you can use the Savings Plans Purchase Analyzer to estimate potential cost savings for custom purchase scenarios. For more information, visit the Database Savings Plans pricing page and the AWS Savings Plans FAQs.

 

​Today, AWS announces expanded coverage for Database Savings Plans, with support for Amazon OpenSearch Service and Amazon Neptune Analytics. With Database Savings Plans, you can save up to 35% in exchange for a commitment to a consistent amount of usage (measured in $/hour) over a one-year term with no upfront payment. Database Savings Plans automatically applies to eligible serverless and provisioned instance usage regardless of supported engine, instance family, size, deployment option, or AWS Region. For example, with Database Savings Plans, you can change from m7i.large.search to c8g.2xlarge.search within OpenSearch Service, or scale Neptune Analytics workloads while continuing to benefit from the discounted pricing. Database Savings Plans for Amazon OpenSearch Service and Amazon Neptune Analytics is available starting today in all AWS Regions, except China Regions. You can get started with Database Savings Plans from the AWS Billing and Cost Management Console or by using the AWS CLI. To realize the largest savings, you can make a commitment to Savings Plans by using purchase recommendations provided in the console. For a more customized analysis, you can use the Savings Plans Purchase Analyzer to estimate potential cost savings for custom purchase scenarios. For more information, visit the Database Savings Plans pricing page and the AWS Savings Plans FAQs.  

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Multi-party approval now supports approval team baselining

Multi-party approval (MPA) now supports MPA administrators running test approvals to confirm that their approval team is set up correctly and that approvers are active and reachable. With this new capability, customers ensure their approval teams do not become unresponsive due to natural attrition, incorrect approver selection, or reduced engagement. MPA administrators and security teams can now proactively assess their approval configurations before relying on them for sensitive operations.

The baseline feature enables proactive team health management by allowing manual initiation of test approval sessions through the AWS Organizations console. Customers can verify approver availability, identify inactive team members, and maintain compliance with internal governance requirements. Key use cases include regular team responsiveness verification, recommended every 90 days by AWS using the MPA Console, onboarding validation for new approval configurations, and operation health checks to ensure approval workflows function effectively when needed.

This feature is available in all AWS commercial regions. To learn more about implementing baseline testing for your multi-party approval workflows, visit the Multi-party approval documentation.    

 

​Multi-party approval (MPA) now supports MPA administrators running test approvals to confirm that their approval team is set up correctly and that approvers are active and reachable. With this new capability, customers ensure their approval teams do not become unresponsive due to natural attrition, incorrect approver selection, or reduced engagement. MPA administrators and security teams can now proactively assess their approval configurations before relying on them for sensitive operations. The baseline feature enables proactive team health management by allowing manual initiation of test approval sessions through the AWS Organizations console. Customers can verify approver availability, identify inactive team members, and maintain compliance with internal governance requirements. Key use cases include regular team responsiveness verification, recommended every 90 days by AWS using the MPA Console, onboarding validation for new approval configurations, and operation health checks to ensure approval workflows function effectively when needed. This feature is available in all AWS commercial regions. To learn more about implementing baseline testing for your multi-party approval workflows, visit the Multi-party approval documentation.      

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AWS Elastic Beanstalk now offers AI-powered environment analysis

AWS Elastic Beanstalk now offers AI-powered environment analysis to help you quickly identify root causes and get recommended solutions for environment health issues. When your environment experiences problems, Elastic Beanstalk collects recent events, instance health, and logs from your environment and sends them to Amazon Bedrock for analysis. This feature is designed for developers and operations teams who need to diagnose and resolve environment issues faster without manually reviewing logs and events.

You can request an AI analysis directly from the Elastic Beanstalk console using the AI Analysis button when your environment’s health status is Warning, Degraded, or Severe. You can also use the AWS CLI with the RequestEnvironmentInfo and RetrieveEnvironmentInfo API operations. The analysis provides step-by-step troubleshooting recommendations tailored to your environment’s current state, helping you reduce mean time to resolution.

AI-powered environment analysis is available in all AWS Regions where both AWS Elastic Beanstalk and Amazon Bedrock are available. 

For more information about the AI-powered environment analysis and for a full list of supported platform versions, see the Elastic Beanstalk developer guide. To learn more about Elastic Beanstalk, visit the Elastic Beanstalk product page.

 

​AWS Elastic Beanstalk now offers AI-powered environment analysis to help you quickly identify root causes and get recommended solutions for environment health issues. When your environment experiences problems, Elastic Beanstalk collects recent events, instance health, and logs from your environment and sends them to Amazon Bedrock for analysis. This feature is designed for developers and operations teams who need to diagnose and resolve environment issues faster without manually reviewing logs and events. You can request an AI analysis directly from the Elastic Beanstalk console using the AI Analysis button when your environment’s health status is Warning, Degraded, or Severe. You can also use the AWS CLI with the RequestEnvironmentInfo and RetrieveEnvironmentInfo API operations. The analysis provides step-by-step troubleshooting recommendations tailored to your environment’s current state, helping you reduce mean time to resolution.
AI-powered environment analysis is available in all AWS Regions where both AWS Elastic Beanstalk and Amazon Bedrock are available. 
For more information about the AI-powered environment analysis and for a full list of supported platform versions, see the Elastic Beanstalk developer guide. To learn more about Elastic Beanstalk, visit the Elastic Beanstalk product page.