Amazon Web Services (AWS) is launching a new collection of developer-focused resources for the AWS Command Line Interface (AWS CLI). These resources demonstrate working end-to-end shell scripts for working with AWS services and best practices that simplify the process of authoring shell scripts that handle errors, track created resources, and perform cleanup operations.
The new AWS Developer Tutorials project on GitHub provides a library of tested, scenario-focused AWS CLI scripts covering over 60 AWS services. These tutorials provide quicker ways to get started using an AWS service API with the AWS CLI. Leveraging generative AI and existing documentation, developers can now more easily create working scripts for their own resources, saving time and reducing errors when managing AWS resources through the AWS CLI. Each script includes a tutorial that explains how the script works with the AWS service API to create, interact with, and clean up resources.
The project also includes instructions that you can use to generate and contribute new scripts. You can use existing content and examples with generative AI tools such as the Amazon Q Developer CLI to generate a working script through an iterative test-and-improve process. Depending on how well-documented the use case is, this process can take as little as 15 minutes. For scenarios that don’t have existing examples of API calls with input and output, it can take more iterations to get a working script. Sometimes you need to provide additional information or examples from your own testing to fill in a gap. This process can actually be quite fun!
Amazon Web Services (AWS) is launching a new collection of developer-focused resources for the AWS Command Line Interface (AWS CLI). These resources demonstrate working end-to-end shell scripts for working with AWS services and best practices that simplify the process of authoring shell scripts that handle errors, track created resources, and perform cleanup operations. The new AWS Developer Tutorials project on GitHub provides a library of tested, scenario-focused AWS CLI scripts covering over 60 AWS services. These tutorials provide quicker ways to get started using an AWS service API with the AWS CLI. Leveraging generative AI and existing documentation, developers can now more easily create working scripts for their own resources, saving time and reducing errors when managing AWS resources through the AWS CLI. Each script includes a tutorial that explains how the script works with the AWS service API to create, interact with, and clean up resources. The project also includes instructions that you can use to generate and contribute new scripts. You can use existing content and examples with generative AI tools such as the Amazon Q Developer CLI to generate a working script through an iterative test-and-improve process. Depending on how well-documented the use case is, this process can take as little as 15 minutes. For scenarios that don’t have existing examples of API calls with input and output, it can take more iterations to get a working script. Sometimes you need to provide additional information or examples from your own testing to fill in a gap. This process can actually be quite fun! To get started, see AWS Developer Tutorials. For more information on the project, see our post on Builder Center.
Today, AWS Resource Explorer has expanded the availability of resource search and discovery to the Asia Pacific (Taipei) AWS Region.
With AWS Resource Explorer you can search for and discover your AWS resources across AWS Regions and accounts in your organization, either using the AWS Resource Explorer console, the AWS Command Line Interface (AWS CLI), the AWS SDKs, or the unified search bar from wherever you are in the AWS Management Console.
For more information about the AWS Regions where AWS Resource Explorer is available, see the AWS Region table.
Today, AWS Resource Explorer has expanded the availability of resource search and discovery to the Asia Pacific (Taipei) AWS Region. With AWS Resource Explorer you can search for and discover your AWS resources across AWS Regions and accounts in your organization, either using the AWS Resource Explorer console, the AWS Command Line Interface (AWS CLI), the AWS SDKs, or the unified search bar from wherever you are in the AWS Management Console. For more information about the AWS Regions where AWS Resource Explorer is available, see the AWS Region table. To turn on AWS Resource Explorer, visit the AWS Resource Explorer console. Read about getting started in our AWS Resource Explorer documentation, or explore the AWS Resource Explorer product page.
5 formas de usar Copilot y herramientas de IA para despertar la curiosidad este año escolar
Por: Equipo de Microsoft Educación.
La temporada de regreso a clases es más que un hito en el calendario: es un momento de anticipación, reflexión y oportunidad. Para los educadores, administradores y personal, es un momento para volver a conectarse con un propósito, reimaginar lo que es posible y prepararse para satisfacer las necesidades cambiantes de los estudiantes. Pero también es un momento de complejidad. Las crecientes demandas, el tiempo limitado y el rápido cambio tecnológico remodelan la educación en tiempo real.
En Microsoft Educación, creemos que la IA en la educación es más que un cambio tecnológico, es una oportunidad humana. Es una oportunidad para potenciar el aprendizaje inclusivo y centrado en el estudiante y ayudar a los educadores a recuperar tiempo para lo que más importa: crear experiencias de aprendizaje impactantes para todos.
Empezar a trabajar con Microsoft 365 Copilot, Copilot Chat y las herramientas con tecnología de IA puede ser fácil, y estamos aquí para apoyar su recorrido con ideas, recursos y ejemplos del mundo real para ayudarlos a generar confianza en el camino.
1. Agilicen su día con Copilot Chat
Los educadores y los líderes educativos tienen una amplia gama de responsabilidades, desde la planificación de la instrucción hasta las comunicaciones estratégicas. Copilot Chat está diseñado para ayudarlos a simplificar esas tareas con un chat seguro impulsado por IA.
¿No están seguros de por dónde empezar? Estas son algunas formas de usar Copilot Chat para ayudarlos con las tareas diarias:
Crear y diferenciar planes de lecciones según el nivel de grado, las preferencias de aprendizaje, las necesidades lingüísticas o los objetivos del plan de estudios.
Diseñar rúbricas y evaluaciones formativas alineadas con los estándares educativos.
Traducir las justificaciones de adaptación del Programa de Educación Individualizada (IEP, por sus siglas en inglés) a un lenguaje sencillo para ayudar a los padres y cuidadores a comprender conceptos complejos.
Hacer una lluvia de ideas y crear activos como agendas, estrategias de comunicación y folletos de redes sociales para eventos escolares.
Redactar comunicaciones profesionales en múltiples formatos para mantener informados a los profesores, el personal, las familias y las comunidades escolares.
Crear guías de incorporación y agendas de aprendizaje profesional para respaldar las transiciones del personal y los objetivos de desarrollo de habilidades.
Escribir propuestas de subvención y presentaciones de la junta por medio de un lenguaje claro y estructurado para diversas audiencias.
Ya sea que estén en proceso de personalizar la instrucción, comunicándose con las partes interesadas o implementar nuevas tecnologías, Copilot Chat ofrece formas de recuperar su tiempo e integrar la IA en su flujo de trabajo diario.
2. Empoderen a los estudiantes adolescentes con Copilot Chat
Copilot Chat está disponible para estudiantes mayores de 13 años, lo que abre nuevas oportunidades para el aprendizaje, la creatividad y el apoyo dentro y fuera del aula. Con una implementación reflexiva, pueden capacitar a los estudiantes para que exploren ideas, hagan preguntas y generen confianza por medio de la IA como un compañero de pensamiento.
Con barreras claras e integración intencional, Copilot Chat también puede apoyar el pensamiento crítico, la alfabetización digital y la curiosidad, habilidades que son cada vez más valiosas tanto en entornos académicos como profesionales. Al guiar a los estudiantes en el uso responsable de la IA, puede ayudarlos a prepararse para navegar por un mundo digital con confianza.
3. Personalicen el aprendizaje con aceleradores de aprendizaje impulsados por IA
Cada estudiante aprende de manera diferente. Los aceleradores de aprendizaje de Microsoft llevan el soporte técnico con tecnología de IA al aula, lo que les ayuda a ofrecer experiencias de aprendizaje más personalizadas. Estas herramientas están diseñadas para fortalecer las habilidades fundamentales, como la fluidez en la lectura y el dominio de las matemáticas, y promover habilidades preparadas para el futuro. También ayudan a simplificar la planificación de lecciones y ofrecen entrenamiento y comentarios en tiempo real para apoyar tanto la enseñanza como el aprendizaje.
Reading Progress, Reading Coach, Math Progress, Speaker Coach y más se integran a la perfección en Microsoft 365 y Microsoft Teams for Education, lo que los ayuda a ustedes y a sus alumnos a empezar con rapidez. Los aceleradores de aprendizaje ofrecen información y visualizaciones de datos para realizar un seguimiento del progreso tanto a nivel de estudiante como de clase, lo que les ayuda a adaptar su instrucción a las necesidades individuales.
4. Generen confianza en la IA con recursos para educadores
Con la orientación adecuada, pueden probar nuevas herramientas, generar confianza y usar la IA de manera que los beneficie a ustedes y a sus estudiantes. Utilicen estos recursos gratuitos diseñados para apoyar su crecimiento y fomentar resultados exitosos para todos:
Kit de herramientas de IA de Microsoft Education: obtengan instrucciones prácticas sobre el uso responsable de la IA, las estrategias de implementación y el aprendizaje profesional. El kit de herramientas de IA está diseñado para ayudar a los educadores de K-20, líderes de TI y tomadores de decisiones a generar confianza y claridad en torno al uso de la IA en la educación.
Centro de recursos educativos de Microsoft: exploren una colección seleccionada de guías de inicio rápido de tecnología flexibles y fáciles de usar, procedimientos y sugerencias de expertos para respaldar sus próximos pasos con IA. Ya sea que desarrollen la alfabetización en IA, comiencen con herramientas impulsadas por IA o escalen la adopción, estos recursos pueden ayudar a convertir el impulso en un progreso significativo.
Centro de educadores de Microsoft Learn: como parte de Microsoft Elevate, ponemos a las personas en primer lugar, al equipar a los educadores con las habilidades, el conocimiento y las herramientas para prosperar con la IA a través del desarrollo profesional y la capacitación que los prepara para liderar en un mundo cambiante.
En todo el mundo, las escuelas e instituciones utilizan las herramientas de IA de Microsoft para reimaginar el aprendizaje, la enseñanza y las operaciones. Estos ejemplos del mundo real muestran cómo la IA puede apoyar el cambio transformador, para ayudar a los profesionales de la educación a individualizar la instrucción, recuperar tiempo y operar de manera más eficiente:
Las escuelas del condado de Fulton, Georgia, en los Estados Unidos, utilizan Copilot Chat para empoderar a los estudiantes adolescentes, agilizar la planificación de lecciones y mejorar la colaboración.
Lo que sigue: Mejorar la instrucción con innovación de IA
Microsoft Learning Zone ya está disponible en versión preliminar pública, una aplicación de aprendizaje gratuita con tecnología de IA creada para que los educadores creen experiencias de aprendizaje personalizadas y adaptables.1 Este centro, que se lanza en PC Copilot+, Microsoft Surface y en todo el ecosistema de Windows, combina la innovación de IA, la ciencia del aprendizaje y los comentarios de los educadores para respaldar el crecimiento profesional.
Microsoft Learning Zone ofrece herramientas para la creación de lecciones, objetivos de aprendizaje personalizables e información basada en datos para ayudar a impulsar la participación de los estudiantes. Obtengan más información sobre Microsoft Learning Zone y explore las actualizaciones de otras herramientas de Microsoft Education en el blog de Tech Community.
Estamos aquí para ayudarlos a prosperar con la IA
Un nuevo año académico marca un nuevo capítulo: una oportunidad para reenfocarse, explorar nuevas posibilidades y prepararse para satisfacer las necesidades cambiantes de los estudiantes. Los educadores lideran este recorrido con dedicación e impacto, y estamos comprometidos a apoyar su trabajo a través de herramientas innovadoras que ayudan a que el aprendizaje sea más atractivo y los resultados más significativos. Juntos, podemos elevar la educación y capacitar a todos los estudiantes para que participen en la oportunidad de la IA.
1 Microsoft Learning Zone está disponible con una licencia de Copilot+ PC y Microsoft Education (A1, A3, A5). La disponibilidad inicial será solo en inglés.
Amazon QuickSight is a fast, scalable, and fully managed Business Intelligence service that lets you easily create and publish interactive dashboards across your organization is now available in Israel (Tel Aviv) and United Arab Emirates (Dubai) Regions. QuickSight dashboards can be authored on any modern web browser with no clients to install or manage; dashboards can be shared with 10s of 1000s of users without the need to provision or manage any infrastructure. QuickSight dashboards can also be seamlessly embedded into your applications, portals, and websites to provide rich, interactive analytics for end-users.
With this launch, QuickSight expands to 25 regions, including: US East (Ohio and N. Virginia), US West (Oregon), Europe (Spain, Stockholm, Paris, Frankfurt, Ireland, London, Milan and Zurich), Asia Pacific (Mumbai, Seoul, Singapore, Sydney, Beijing, Tokyo and Jakarta), Canada (Central), South America (São Paulo), Africa (Cape Town), AWS GovCloud (US-East, US-West), and now Israel (Tel Aviv) and United Arab Emirates (Dubai).
Amazon QuickSight is a fast, scalable, and fully managed Business Intelligence service that lets you easily create and publish interactive dashboards across your organization is now available in Israel (Tel Aviv) and United Arab Emirates (Dubai) Regions. QuickSight dashboards can be authored on any modern web browser with no clients to install or manage; dashboards can be shared with 10s of 1000s of users without the need to provision or manage any infrastructure. QuickSight dashboards can also be seamlessly embedded into your applications, portals, and websites to provide rich, interactive analytics for end-users. With this launch, QuickSight expands to 25 regions, including: US East (Ohio and N. Virginia), US West (Oregon), Europe (Spain, Stockholm, Paris, Frankfurt, Ireland, London, Milan and Zurich), Asia Pacific (Mumbai, Seoul, Singapore, Sydney, Beijing, Tokyo and Jakarta), Canada (Central), South America (São Paulo), Africa (Cape Town), AWS GovCloud (US-East, US-West), and now Israel (Tel Aviv) and United Arab Emirates (Dubai). To learn more about Amazon QuickSight, please see our product page, documentation and available regions here.
Today, AWS End User Messaging announces support for international sending for US toll-free numbers. International sending support allows customers to send SMS messages to 150+ country destinations including Canada using their US toll-free numbers. With this this launch, customers can leverage a single phone number to send to many supported country destinations globally simplifying their account and resource setup.
AWS End User Messaging provides developers with a scalable and cost-effective messaging infrastructure without compromising the safety, security, or results of their communications. Developers can integrate messaging to support uses cases such as one-time passcodes (OTP) at sign-ups, account updates, appointment reminders, delivery notifications, promotions and more.
Support for international sending for US toll-free numbers is available in all AWS Regions where End User Messaging is available, see the AWS Region table.
Today, AWS End User Messaging announces support for international sending for US toll-free numbers. International sending support allows customers to send SMS messages to 150+ country destinations including Canada using their US toll-free numbers. With this this launch, customers can leverage a single phone number to send to many supported country destinations globally simplifying their account and resource setup. AWS End User Messaging provides developers with a scalable and cost-effective messaging infrastructure without compromising the safety, security, or results of their communications. Developers can integrate messaging to support uses cases such as one-time passcodes (OTP) at sign-ups, account updates, appointment reminders, delivery notifications, promotions and more. Support for international sending for US toll-free numbers is available in all AWS Regions where End User Messaging is available, see the AWS Region table. To learn more, see AWS End User Messaging.
Amazon Managed Service for Prometheus, a fully managed Prometheus-compatible monitoring service now sends alerts directly to PagerDuty, making it easier to manage your incident notifications. You no longer need to create custom Lambda functions or set up additional services to connect with PagerDuty. This direct integration makes alert delivery more reliable and simplifies the authentication process.
This feature is now available in all AWS regions where Amazon Managed Service for Prometheus is generally available. To configure PagerDuty as a receiver for your Amazon Managed Service for Prometheus alerts, visit the Alert manager tab in the AWS console for Amazon Managed Service for Prometheus or use the AWS CLI, SDK, or APIs. Check out the Amazon Managed Service for Prometheus user guide for detailed documentation.
Amazon Managed Service for Prometheus, a fully managed Prometheus-compatible monitoring service now sends alerts directly to PagerDuty, making it easier to manage your incident notifications. You no longer need to create custom Lambda functions or set up additional services to connect with PagerDuty. This direct integration makes alert delivery more reliable and simplifies the authentication process.
This feature is now available in all AWS regions where Amazon Managed Service for Prometheus is generally available. To configure PagerDuty as a receiver for your Amazon Managed Service for Prometheus alerts, visit the Alert manager tab in the AWS console for Amazon Managed Service for Prometheus or use the AWS CLI, SDK, or APIs. Check out the Amazon Managed Service for Prometheus user guide for detailed documentation.
AWS announces Amazon EMR S3A, a new Amazon S3 connector that optimizes performance for Apache Hadoop, Apache Spark, and Apache Hive workloads on Amazon EMR. This new connector enhances the open source S3A architecture with AWS-specific optimizations to help organizations process large-scale data more efficiently. With direct integration support for S3 Express One Zone, S3 Glacier, and AWS Outposts, EMR S3A helps customers leverage different storage options in AWS to optimize both data access speed and storage cost on their EMR workloads.
Additionally, the EMR S3A connector delivers advanced security features and performance capabilities that extend beyond open source S3A. Key improvements include Apache Spark built-in fine-grained access control support, enhanced S3A credentials resolver, MagicCommitter V2 for optimized file writes, and accelerated S3 prefix listing for columnar file formats. These enhancements are available starting with EMR release 7.10 and maintain compatibility with existing applications.
The Amazon EMR S3A connector is available in all AWS Regions where Amazon EMR is available and comes pre-configured with Amazon EMR release version 7.10 and later. To learn more about Amazon EMR S3A, see the Amazon EMR documentation.
AWS announces Amazon EMR S3A, a new Amazon S3 connector that optimizes performance for Apache Hadoop, Apache Spark, and Apache Hive workloads on Amazon EMR. This new connector enhances the open source S3A architecture with AWS-specific optimizations to help organizations process large-scale data more efficiently. With direct integration support for S3 Express One Zone, S3 Glacier, and AWS Outposts, EMR S3A helps customers leverage different storage options in AWS to optimize both data access speed and storage cost on their EMR workloads. Additionally, the EMR S3A connector delivers advanced security features and performance capabilities that extend beyond open source S3A. Key improvements include Apache Spark built-in fine-grained access control support, enhanced S3A credentials resolver, MagicCommitter V2 for optimized file writes, and accelerated S3 prefix listing for columnar file formats. These enhancements are available starting with EMR release 7.10 and maintain compatibility with existing applications.
The Amazon EMR S3A connector is available in all AWS Regions where Amazon EMR is available and comes pre-configured with Amazon EMR release version 7.10 and later. To learn more about Amazon EMR S3A, see the Amazon EMR documentation.
Amazon EMR on EC2 announces two significant enhancements for governance: Apache Spark native fine-grained access control (FGAC) via AWS Lake Formation, and support for AWS Glue Data Catalog views. These features allow organizations to improve data security, simplify access management, and enhance data sharing capabilities across their analytics environments.
The Apache Spark native FGAC implementation allows customers to define granular access policies once in AWS Lake Formation and apply them consistently across EMR clusters. This reduces security risks and administrative overhead while providing a unified approach to data governance. Customers can now use familiar Lake Formation grant and revoke statements to manage access controls for their Spark jobs and interactive sessions on EMR on EC2, similar to how this works for other AWS analytics services.
AWS Glue Data Catalog views enables customers to create, manage, and query multi-engine SQL views across AWS regions, accounts, and organizations. This feature allows administrators to create views from Spark jobs that can be queried from multiple engines, while controlling data access through Lake Formation permissions. These permissions include named resource grants, data filters, and tags, with all access requests automatically logged in AWS CloudTrail for comprehensive auditing.
Amazon EMR on EC2 announces two significant enhancements for governance: Apache Spark native fine-grained access control (FGAC) via AWS Lake Formation, and support for AWS Glue Data Catalog views. These features allow organizations to improve data security, simplify access management, and enhance data sharing capabilities across their analytics environments. The Apache Spark native FGAC implementation allows customers to define granular access policies once in AWS Lake Formation and apply them consistently across EMR clusters. This reduces security risks and administrative overhead while providing a unified approach to data governance. Customers can now use familiar Lake Formation grant and revoke statements to manage access controls for their Spark jobs and interactive sessions on EMR on EC2, similar to how this works for other AWS analytics services. AWS Glue Data Catalog views enables customers to create, manage, and query multi-engine SQL views across AWS regions, accounts, and organizations. This feature allows administrators to create views from Spark jobs that can be queried from multiple engines, while controlling data access through Lake Formation permissions. These permissions include named resource grants, data filters, and tags, with all access requests automatically logged in AWS CloudTrail for comprehensive auditing. Apache Spark native FGAC and Glue Data Catalog view features are available with Amazon EMR release 7.10 in all AWS Regions where EMR on EC2 is available. To learn more, visit Using AWS Lake Formation with Amazon EMR and Working with AWS Glue Data Catalog Views in the Amazon EMR documentation.
Amazon SageMaker introduces account-agnostic, reusable project profiles (templates) in Amazon SageMaker Unified Studio domain, enabling domain administrators to define project configurations once and reuse them across multiple AWS accounts and regions. Project profiles are no longer tied to a specific AWS account or region. Instead, platform teams can reference an account pool—a new domain entity that enables dynamic account and region selection at the time of project creation, based on custom enterprise authorization policies or user-specific logic. This decoupling of profile definitions from static deployment settings simplifies governance, reduces duplication, and accelerates onboarding across large-scale data and ML environments.
Project creators benefit from a more flexible experience: during project creation, they can select from a personalized list of authorized AWS accounts and regions, powered by custom resolution strategies or predefined account pools. This model supports organizations operating across hundreds or thousands of accounts, while preserving centralized control and permission boundaries.
This feature is now available in all AWS Regions where Amazon SageMaker Unified Studio is supported.
Amazon SageMaker introduces account-agnostic, reusable project profiles (templates) in Amazon SageMaker Unified Studio domain, enabling domain administrators to define project configurations once and reuse them across multiple AWS accounts and regions. Project profiles are no longer tied to a specific AWS account or region. Instead, platform teams can reference an account pool—a new domain entity that enables dynamic account and region selection at the time of project creation, based on custom enterprise authorization policies or user-specific logic. This decoupling of profile definitions from static deployment settings simplifies governance, reduces duplication, and accelerates onboarding across large-scale data and ML environments.
Project creators benefit from a more flexible experience: during project creation, they can select from a personalized list of authorized AWS accounts and regions, powered by custom resolution strategies or predefined account pools. This model supports organizations operating across hundreds or thousands of accounts, while preserving centralized control and permission boundaries. This feature is now available in all AWS Regions where Amazon SageMaker Unified Studio is supported.
To learn more about account-agnostic project profiles in Amazon SageMaker refer to account pools in Amazon SageMaker Unified Studio.
The Amazon SageMaker lakehouse architecture now supports tag based access control (TBAC) for managing fine-grained data access across federated catalogs. This capability, previously available only for default AWS Glue Data Catalog resources, is now available across Amazon S3 Tables, Amazon Redshift data warehouses, and federated data sources including Amazon DynamoDB, PostgreSQL, and SQL Server. TBAC enables simplified permission management by logically grouping catalog resources using tags, allows scaling permissions across datasets with a minimal set of permissions, and also facilitates data sharing across different accounts.
TBAC simplifies how administrators manage data access permissions by replacing direct resource-level permissions with tag-based grants. Instead of manually assigning permissions to individual tables or columns, administrators can now efficiently control access through tags that are automatically inherited by resources. This inheritance feature ensures that new tables automatically receive appropriate fine-grained access controls without additional policy modifications.
You can get started with TBAC through the AWS Lake Formation console. Create tags using key-value pairs, associate them with databases, tables, or columns, and grant permissions to principals based on specific tags. Users can then access tagged resources through Amazon Athena, Amazon Redshift, Amazon EMR, or Amazon SageMaker Unified Studio.
This feature is available through the AWS Management Console, AWS CLI, and AWS SDKs in all commercial AWS Regions. To get started, read the blog and visit the Lake Formation Tags documentation.
The Amazon SageMaker lakehouse architecture now supports tag based access control (TBAC) for managing fine-grained data access across federated catalogs. This capability, previously available only for default AWS Glue Data Catalog resources, is now available across Amazon S3 Tables, Amazon Redshift data warehouses, and federated data sources including Amazon DynamoDB, PostgreSQL, and SQL Server. TBAC enables simplified permission management by logically grouping catalog resources using tags, allows scaling permissions across datasets with a minimal set of permissions, and also facilitates data sharing across different accounts. TBAC simplifies how administrators manage data access permissions by replacing direct resource-level permissions with tag-based grants. Instead of manually assigning permissions to individual tables or columns, administrators can now efficiently control access through tags that are automatically inherited by resources. This inheritance feature ensures that new tables automatically receive appropriate fine-grained access controls without additional policy modifications. You can get started with TBAC through the AWS Lake Formation console. Create tags using key-value pairs, associate them with databases, tables, or columns, and grant permissions to principals based on specific tags. Users can then access tagged resources through Amazon Athena, Amazon Redshift, Amazon EMR, or Amazon SageMaker Unified Studio. This feature is available through the AWS Management Console, AWS CLI, and AWS SDKs in all commercial AWS Regions. To get started, read the blog and visit the Lake Formation Tags documentation.
