AWS Transform FAQs

At launch, AWS Transform is designed to support large-scale porting of .NET Framework applications to cross-platform .NET, modernization and migration of COBOL applications on mainframe to Java applications on AWS, and migration and modernization of VMware workloads to Amazon EC2.

These capabilities are available in a unified web experience tailored for large-scale modernization and team collaboration at https://console.aws.amazon.com/transform/home

AWS Transform  uses generative AI and machine learning (ML) algorithms to provide you with a more intelligent, adaptive, and automated migration experience through a natural language chat interface. Unlike traditional tools that often require extensive manual input and decision-making, AWS Transform can autonomously analyze your environment, suggest optimal migration strategies, and adapt plans in real-time based on changing conditions. AWS Transform continually learns from each migration, improving recommendations and automation processes. This results in faster, more accurate migration planning and execution, reducing the reliance on scarce migration expertise and minimizing the risk of human error.

AWS Transform supports porting .NET Framework applications to cross-platform Linux-ready .NET, modernizing COBOL applications on mainframes to Java applications on AWS, and moving virtualized workloads on VMware to scalable workloads on Amazon EC2.

To get started, sign in to the AWS Transform web experience with your current enterprise credentials. If you are a new customer, you can use single sign-on (SSO) with AWS IAM Identity Center integration and connect it to an AWS account to get started.

• For .NET porting, you can connect to your source code repository in GitHub. AWS Transform scans your linked repository, finds suitable projects, and lets you customize selections. Choose projects to modernize with admin approval. Once approved, the agent automatically ports your .NET applications to the selected version, from Windows to Linux. You can monitor the transformation's progress through the dashboard and worklogs. AWS Transform commits the transformed code to a new branch in your repository once the task is complete, preserving the original source code.

• For mainframe application modernization, you can provide AWS Transform with some of your existing mainframe application code, which it will use to assess the code base. Using its underlying LLM, AWS Transform creates comprehensive documentation to understand and expand the knowledge base of your organization. AWS Transform agents decompose large monoliths into simple and loosely coupled business domains, making the systems more agile and easier to maintain. Next, you define your high-level modernization objectives using natural language. AWS Transform builds a comprehensive action plan to refactor your mainframe codebase to Java and deploy it on AWS services like Amazon Elastic Compute Cloud (Amazon EC2), Amazon Relational Database Service (Amazon RDS), and AWS Fargate. AWS Transform agents work autonomously, notifying you of ongoing or completed actions, and blockers requiring your attention.

• For VMware migrations, AWS Transform will guide you to add connectors to your source VMware environment or upload your asset inventory from third-party tools. You can start a new project in AWS Transform by specifying your goals.

Yes, AWS Transform is designed to migrate your complex, multi-tier applications. Its graph neural network technology identifies intricate application dependencies and relationships, even in large, complex environments. It then groups related servers into logical application groups that need to be migrated as a single migration wave. For instance, when migrating a 500 VM environment, AWS Transform may identify that 50 VMs need to be migrated as a single unit due to tight coupling. This capability is particularly valuable for customers with interconnected legacy systems or microservices architectures. You can also download the application groupings generated by AWS Transform, review and edit them if needed, and upload the updated groupings back to AWS Transform to continue their migration.

Yes, AWS Transform uses a human-in-the-loop mechanism to allow authorized users to review, approve, and edit artifacts that it generates. For example, once AWS Transform generates a migration wave plan, authorized users will receive a “Collaboration” request to review and approve the wave plan, including the mapping of servers to waves and sequencing of waves. To update the wave plan, AWS Transform provides users an option to export the data in CSV format, edit it, and import the updated dataset for AWS Transform to continue the migration job.

AWS Transform assessments analyzes your IT environment to simplify and optimize your cloud journey with intelligent, data-driven insights and actionable recommendations. Discover cost and performance optimization opportunities while getting detailed financial modeling to help you confidently plan your migration and maximize potential savings.

The workflow begins with uploading your existing server inventory to the AWS Transform platform. Once your data is in place, you have the opportunity to specify your target AWS Region. Next, you can then instruct AWS Transform to generate your business case. AWS Transform analyzes your server inventory and identifies the most suitable and cost-effective Amazon EC2 instances for each one. The resulting business case provides you with a clear, data-driven projection of how your current on-premises environment could map to AWS services, offering valuable insights for your migration planning and decision-making process.

AWS Transform supports a variety of data collection methods for x86 servers, whether virtual or physical, from on-premises environments. The service accepts server inventory data from several widely used assessment tools. These include exports from RVTools, data collected through the AWS Migration Evaluator agentless collector, and AWS Migration Portfolio Assessment (MPA) export formats generated by tools like modelizeIT and Cloudamize.

After the completion of the assessment job, AWS Transform provides a summary of the assessment, an opportunity to ask questions about the cost and recommendations and the option to download a PDF version of the business case for offline review and sharing.

The business case includes key highlights from the server inventory, a summary of current infrastructure, and multiple total cost of ownership (TCO) scenarios with varying purchase commitments (on-demand and reserved instances), operating system licensing options (bring your own licenses and license-included) and tenancy options (dedicated and shared). The business case also includes actionable next step recommendations.

AWS Transform assessments provide directional estimates that approximate the cost of AWS services based on your current server configurations and assumed usage patterns. While these estimates are helpful for initial planning purposes, they should be viewed as guidance rather than exact figures. Actual AWS costs may vary depending on your specific implementation, resource optimization choices, and real-world usage patterns. It's important to note that these estimates are not quotes and should not be interpreted as guarantees of your final AWS service costs. For more precise cost planning, we recommend working with your AWS account team or an AWS Partner who can help analyze your specific requirements and usage patterns in detail.

AWS Transform Assessment and AWS Migration Evaluator are both valuable tools for planning cloud migrations. Assessments is a fast, self-service capability of AWS Transform, designed specifically for organizations looking to migrate x86 servers from on-premises environments to AWS. It utilizes existing server inventory data to provide targeted recommendations for Amazon EC2 instances and generate quick TCO estimates. This streamlined approach is ideal for companies seeking a rapid, focused assessment of their migration options. AWS Migration Evaluator offers a more comprehensive, expert-led assessment service. Guided by AWS Solutions Architects, this in-depth evaluation encompasses a broader range of analyses, including detailed data collection, storage assessment, sustainability evaluation, and Microsoft SQL Server analysis. Migration Evaluator is best suited for organizations that require thorough migration planning and desire expert guidance throughout the process.

AWS Transform has a built-in AI chat capability so you can ask for more details or clarification about instance mapping, licensing and tenancy suggestions, and next step recommendations. For further support or additional analysis for other workload types, engage with your account team or partner or contact us.

With AWS Transform for .NET, you can accelerate transformation time by 4X compared to manual porting and reduce licensing costs by as much as 40%. AWS Transform for .NET provides this acceleration through its ability to transform hundreds of applications simultaneously with human-in-the-loop (HITL) supervision.

AWS Transform for .NET accelerates modernization of Windows-based .NET Framework applications to cross-platform .NET for Linux environments. It connects to your source code repositories in GitHub, GitLab, or Bitbucket and performs a comprehensive analysis focused on three key areas: repository dependencies, required private packages, and third-party libraries, as well as identifying supported project types.

Based on this analysis, it generates a transformation plan for these repositories and highlights any missing dependencies that you can resolve by uploading packages yourself. During the transformation process, AWS Transform for .NET converts application code, builds the output, runs unit tests, and commits results to a new branch in your repository.

It provides a comprehensive transformation summary, including modified files, test outcomes, and suggested fixes for any remaining work. Your teams can track transformation status through AWS Transform dashboards or use its interactive chat. Additionally, your teams receive email notifications with links to transformed .NET code. For workloads that need further refinement, your developers can continue using the Visual Studio extension in AWS Transform.

During the analysis phase, AWS Transform discovers the repositories in your account and identifies supported project types in each repo. It supports porting console applications, class libraries, Web APIs, WCF Services, MVC (Model View Controller), SPA (Single Page Application), and unit test projects (XUnit, NUnit, MSTest frameworks) to cross-platform .NET. In addition, AWS Transform also ports MVC Razor views UI projects to ASP.NET Core Razor views.

After identifying project types, it analyzes these projects for dependencies on other projects, private packages, and third-party libraries. Based on the dependency analysis, AWS Transform recommends a transformation plan that orders repositories according to their last modification dates, dependency relationships, and private package requirements.

You can download the analysis report to evaluate the recommended plan and review it with your team. You also have the option to customize the recommended plan by editing the selection in the console or by uploading a modified file with your preferred selection. Administrators and approvers can review and approve the plan before proceeding with the transformation process.

During transformation, the selected source code repositories from your approved plan are securely fetched into a network-isolated execution environment for porting to cross-platform .NET. AWS Transform for .NET supports transforming applications written using .NET Framework versions 3.5+, .NET Core 3.1, .NET 5, .NET 6, and .NET 7 to cross-platform .NET 8 (LTS).

After porting, AWS Transform runs a full .NET build to identify any build errors and runs an AI-led evaluation loop to auto-remediate issues. This process is repeated across all supported projects within the repositories. After the transformation job is completed, the transformed code is committed back to your source code repository in your chosen target branch for review.

For repositories that have successfully completed transformations with zero build errors, AWS Transform executes unit test projects, if present, and provides those execution results for your review. For repositories that have partially transformed projects, unit test projects are ported but are not run. You can resolve remaining issues yourself before running the unit tests.

AWS Transform for .NET commits the transformed code under a new target branch that you specified in the job. You can review the source code branch in your repositories to identify the code changes suggested by AWS Transform.

Additionally, AWS Transform for .NET provides a detailed natural language summary of transformation actions on each of your projects, including the list of files that were modified and moved. If any projects are partially transformed, the transformation summary report will include details on the specific build errors encountered and recommendations for resolving those issues.

You can track porting progress of your repositories in real-time via the dashboard and worklogs. Worklogs provide detailed logs of all actions that AWS Transform is performing on your source code, allowing you to audit the actions and approvals provided by workspace users.

You can also chat with AWS Transform to get the latest status on the transformation of a repository or project. Upon completion, AWS Transform commits the ported code into a new target branch in your repository.

When the transformation job is completed, you will receive an email notification with deep links to the repositories to checkout and review the changes made by AWS Transform. For projects that require further changes, you can continue the transformation work in Visual Studio IDE and make modifications to meet your needs.

You are the owner of the code ported by AWS Transform .NET agent. Once the porting is completed, you can review the branch where the ported code is committed and either modify the ported source code or use it as-is prior to deploying into production.

AWS Transform .NET agent analyzes your code to identify inter-project dependencies and also private packages used within the projects to recommend a transformation plan. The service is designed to securely and ephemerally clone your .NET solution, allowing you to use customer managed KMS keys (CMK) for encrypting your code in this environment. CMK allows you to have full control over keys, including managing policies, grants, tags and aliases accessing data. 

Your source code processed by AWS Transform is stored only for the duration of the job and purged after the job is completed. Your trust, privacy, and the security of your content are our highest priority. We implement appropriate controls, including encryption in transit, to prevent unauthorized access to, or disclosure of, your content and ensure that our use complies with our commitments to you. 

AWS Transform for mainframe is a new agentic AI-powered service designed to accelerate the modernization of legacy mainframe applications. Customers can define high-level modernization goals and leverage a specialized AI agent to orchestrate the necessary tools and processes. The agent analyzes codebases, generates documentation, extracts business logic, decomposes monolithic structures, transforms legacy code, and manages modernization tasks, offering human-in-the-loop oversight where desired.

Key capabilities of AWS Transform include goal-driven reasoning, classification of application assets, planning and documentation generation with business logic extraction, and automated refactoring that converts COBOL-based mainframe workloads into modern, cloud-optimized Java applications.

AWS Transform empowers customers to modernize their critical mainframe applications faster, more cost-effectively, and with confidence that their business-critical logic will be preserved throughout the transformation process.

To help ensure your application is complete for modernization, you can leverage AWS Transform’s classification and missing asset identification capabilities. The service can analyze your existing mainframe codebase, categorizing application assets such as programs, languages, screens, data structures, and configurations.

Importantly, AWS Transform can also identify any missing elements, streamlining the modernization process. The specialized agent autonomously reviews your code and artifacts, organizing millions of assets and detecting missing dependencies. It also generates dependency graphs for easier visualization of the application's modules and complexities. Using the business logic extraction, you can engage your business stakeholders in the project to better understand the impacts of different areas of the code you are modernizing. This comprehensive understanding of your legacy systems is a prerequisite and key input for engaging in the decomposition and planning activities that are central to the modernization journey.

A key feature of AWS Transform is its ability to break down monolithic mainframe applications into modular, business-aligned domains, and then generate comprehensive modernization waves. Using the business logic extraction in conjunction with the decomposition step helps break down monoliths into logical business domains. Leveraging automated reasoning and planning capabilities, AWS Transform analyzes your codebase, identifies discrete functional areas, and organizes the application assets accordingly. It then creates detailed, prioritized modernization plans that consider factors like business priorities, technical complexity, and constraints. This domain-driven decomposition and thoughtful planning allows you to tackle the modernization in manageable, iterative steps. By providing this visibility and structure up front, AWS Transform empowers you to focus your efforts, make informed decisions, and execute the modernization quicker.

Yes, AWS Transform for mainframe is modular, allowing you to leverage its capabilities for as many or as few phases of the modernization journey as you choose. For example, you might initially use the code analysis and documentation features and later choose to incorporate a certain domain through decomposition, transformation, and reforge steps.

Inventory collection encompasses various mainframe components including COBOL programs, copybooks, Job Control Language (JCL), procedures and parameter cards, and DB2 definitions. If available, Customer Information Control System (CICS) and CSD files should be loaded to determine entry points.

The extraction process begins by downloading source elements through text mode, converting each member into individual source files. Files should be organized in a structured directory system that reflects their origin, language, type, and application/sub-application relationships (for example, C:\Mainframe\APP1\Cobol\Program1.CBL or \Mainframe\APP1\JCL\JCL1.txt). If no file extension is provided, AWS Transform will determine the appropriate extension based on the file contents to classify the member.

The collected inventory is then compressed into a zip file and uploaded to an S3 bucket. The process might be iterative, with an initial upload followed by subsequent iterations of missing components until reaching satisfactory completeness.

After code transformation, you have the option to use pre-built Infrastructure as Code (IaC) templates to deploy your modernized applications. These templates are accessible through the AWS Transform chat interface, helping create the necessary compute resources, databases, storage, and security controls. Templates are available in AWS CloudFormation, AWS CDK, and Terraform formats.

You can also use the Reforge step to enhance your transformed Java code with improved readability and maintainability before deployment. To use this feature, provide your refactored code and java class list to specify which service classes to reforge. AWS Transform will generate downloadable files containing the reforged code.

AWS Transform provides the ability to specify files within your source code to generate documentation. You can choose summary overviews of file collections or detailed functional specifications for each file. The detailed specifications include logic flows, input/output processing, and other transactional details.

Once generated, you can access this documentation by viewing files in the AWS Transform interface or downloading them in XML or PDF formats. Additionally, the AWS Transform chat function allows you to query the documentation to better understand your documents, such as asking about specific file purposes or functionality.

The Analyze step, required for all mainframe jobs, examines source code provided in the S3 bucket and generates several key insights. AWS Transform classifies file types and provides metrics including total lines of code, comment lines of code, effective lines of code, and cyclomatic complexity (representing the number of linearly independent paths through program's source code). The analysis identifies missing and duplicated files, including files that share the same name or program ID. It also generates dependency mapping between files to be used during the decomposition step. This information helps you understand the state of your source code before proceeding with modernization.

AWS Transform makes mainframe modernization more accessible to business stakeholders through automated business logic extraction. This capability extracts from the source code the business rules, the functional groups, the entry points helping stakeholders to retrieve the lost knowledge about the business logic of their application. Additionally, developers can leverage these insights to quickly understand legacy system functionality without deep mainframe expertise.

AWS Transform for VMware provides three key advantages. First, AWS Transform orchestrates your entire migration journey, boosting team productivity. Second, it automates complex and labor- intensive migration tasks, including wave planning and network conversion. This simplification accelerates migrations, reduces errors, and minimizes the need for in-house expertise, fast- tracking your time to value. Finally, AWS Transform customizes your migration journey by understanding your specific migration goals and analyzing your source environment.

Yes, AWS Transform for VMware is designed to migrate your complex, multi-tier applications. Its Graph Neural Network technology identifies intricate application dependencies and relationships, even in large, complex environments. It then groups related servers into logical application groups that need to be migrated as a single migration wave. For instance, when migrating a 500 VM environment, AWS Transform might identify that 50 VMs need to be migrated as a single unit due to tight coupling. This capability is particularly valuable for customers with interconnected legacy systems or microservices architectures. You can also download the application groupings generated by AWS Transform, review and edit them if needed, and upload the updated groupings back to AWS Transform to continue their migration.

To get started, sign in to the AWS Transform web application using your current enterprise credentials. If you are a new user, your account administrator must first enable AWS Transform and add you as a user through AWS IAM Identity Center for single sign-on (SSO) access. For VMware migrations, AWS Transform will guide you to add connectors to your source VMware environment or upload your asset inventory from third-party tools. You can start a new project in AWS Transform by specifying your goals.

AWS Transform for VMware is the first generative AI–powered assistant for large-scale migration of VMware workloads to Amazon Elastic Compute Cloud (Amazon EC2). It simplifies and accelerates your migration by allowing you to specify goals, generate plans to meet those goals, conduct approved actions on your behalf, and track migration progress through comprehensive dashboards.

AWS Transform for VMware streamlines the entire migration journey by analyzing your environment, builds an understanding of your application inventory and dependencies, and proposes logical application groups for migration waves using server and network data. It orchestrates dependency-aware migrations to minimize downtime, recommends right-sized Amazon EC2 instances, and allows for seamless collaboration across teams. With AWS Transform for VMware, multiple stakeholders can collaborate on migrations while maintaining a unified view of progress through intuitive dashboards.

AWS Transform offers four types of VMware migration job plans to meet your specific needs:

• End-to-end migration: Performs discovery, generates wave plans, configures VPC networks, and migrates servers.
• Network migration only: Focuses solely on generating and deploying VPC configurations.
• Network-and-server migration: Configures and deploys VPC networks, then migrates servers without discovery.
• Discovery and server migration: Performs discovery, generates wave plans, and migrates servers without network configuration.

Yes, AWS Transform analyzes the configuration and utilization data of your source VMs to recommend appropriate EC2 instance types for your migrated workloads. It considers factors like CPU, memory, storage, and network requirements to suggest cost-effective and performance-optimized instances. You can review and adjust these recommendations before migration.

AWS Transform for VMware helps you discover source servers using multiple data collection methods. It plans your migration to AWS using the configuration data collected about your source servers and databases, applying machine learning (ML) techniques, such as Graph Neural Networks, to plan your migration waves. It supports several ways of performing discovery and collecting data about your source servers.

Using AWS Application Discovery Service, AWS Transform can conduct agentless discovery by deploying the Application Discovery Service Agentless Collector (OVA file) through your VMware vCenter. The Agentless Collector can discover VM configuration and utilization, database metadata and utilization, and network connections. It can also conduct agent-based discovery by deploying the Application Discovery Agent on each of your VMs and physical servers. The agent installer is available for Windows and Linux operating systems. It collects configuration data, utilization data, inbound and outbound network connections, and processes that are running.

It can also conduct agent-based discovery by deploying the Application Discovery Agent on each of your VMs and physical servers. The agent installer is available for Windows and Linux operating systems. It collects configuration data, utilization data, inbound and outbound network connections, and processes that are running.

In addition, you can also use RVTools exports to provide CSV or Excel format exports that contain detailed information about your VMware environment including vSwitches, port groups, and VLANs. You can also export discovery data from select third-party tools to be used in AWS Transform for migration planning.

To get started, designate an AWS account as your discovery account and connect it to the VMware modernization capabilities of AWS Transform. You can download, view, and analyze all collected data directly within the AWS Transform console.

Currently, AWS Transform only supports migrating source VMware environments to Amazon EC2. While AWS Transform does not support automated migration of source VMware environments to Amazon Elastic VMware Service (EVS), it understands your migration goals and provides guidance on migrating to EVS by using VMware Hybrid Cloud Extension (HCX) for your use case.

AWS Transform for VMware implements comprehensive encryption for your data both in transit and at rest:

Data in transit:

• All communications between your environment, AWS Transform for VMware, and AWS services use Transport Layer Security (TLS) 1.2 or higher encryption.

• Data replication from your source servers to AWS utilizes encrypted connections for secure transfer.

• API calls between AWS services involved in your migration are automatically encrypted as part of AWS standard security practices.

Data at rest:

• By default, AWS Transform for VMware encrypts data stored in Amazon S3 buckets using AWS managed encryption keys.

• You have the option to use your own customer-managed AWS KMS keys for enhanced control and security over the encryption process.

• Replicated server data stored during migration is encrypted according to AWS Application Migration Service standard encryption practices.

• Metadata and configuration information stored by AWS Transform for VMware is encrypted using AWS standard encryption mechanisms.

This comprehensive encryption approach helps ensure your migration data remains protected throughout the entire migration process, aligning with security best practices and helping you meet compliance requirements for data protection.

Important Note: AWS Transform creates Amazon S3 buckets on your behalf in your source and target AWS accounts. These buckets do not have SecureTransport enabled by default. If you want the bucket policy to include SecureTransport, you must update the policy yourself. For more information, see Security best practices for Amazon S3.

Yes, AWS Transform for VMware lets you avoid using the public internet for data replication. You can establish private connectivity using AWS Direct Connect for a dedicated, high-bandwidth link or an AWS Site-to-Site VPN for an encrypted tunnel between your data center and AWS. These options keep migration traffic secure and off the public internet while improving performance with more predictable network conditions. When setting up replication, you can configure AWS Transform to use your private connection, making it ideal for large-scale migrations with sensitive or high-volume data.

AWS Transform for VMware stores your migration data in several places:

• Your AWS accounts: AWS Transform creates S3 buckets in both your discovery and target accounts to store your migration data, artifacts, and configuration information. You maintain full control over these buckets and can choose the encryption keys used.
• AWS Transform workspace: Your data is processed in the AWS Region where you created your AWS Transform workspace (either US East (N. Virginia) or Europe (Frankfurt)) to generate migration recommendations and plans.
• Temporary service storage: For certain migration jobs, customer data is securely and temporarily uploaded to an artifact store in the AWS service account in the same region as your source account. This data is used for processing and is automatically deleted if the job or account is deleted.
• Service metrics storage: Calculated migration metrics and assessment results are stored in AWS service accounts in S3 and CloudWatch for service improvement and operational monitoring.

While AWS Transform creates S3 buckets with basic security configurations including encryption at rest, we strongly recommend implementing additional S3 bucket security best practices to fully protect your data, such as blocking public access, enforcing encryption in transit, enabling access logging, and implementing appropriate bucket policies.

AWS Transform for VMware operates in two distinct ways when it comes to Regional availability:

Workspace Regions: These Regions host the AI workspaces where discovery data is processed, assessments are conducted, wave planning occurs, and right-sizing recommendations are generated. Currently, workspace Regions include:

• US East (N. Virginia)
• Europe (Frankfurt)

You should choose your workspace Region based on compliance requirements for data processing. For example, European customers with data residency requirements should select Europe (Frankfurt) to ensure their configuration data remains within Europe during analysis.

Target migration Regions: AWS Transform for VMware supports migration to these target Regions.

Under the shared responsibility model, you are responsible for selecting the appropriate Regions that meet your data residency and compliance requirements. If you choose a target Region that differs from your workspace Region, be aware that data will be transferred across AWS Regions during the migration process, and you'll need to evaluate this against your data governance policies.

For the most up-to-date information on supported Regions, refer to AWS Services by Region.

We may use certain Content from AWS Transform for service improvement. AWS Transform may use this content, for example, to provide better responses to common questions, fix AWS Transform operational issues, for de-bugging, or for model training.

You can opt out of service improvement at any time through your service settings. For the AWS Transform web console experience, opt out by configuring an AI services opt-out policy in AWS Organizations. For more information, see AI services opt-out policies in the AWS Organizations User Guide. For the IDE, adjust your settings in the IDE to opt out.

Your trust, your privacy, and the security of your data are our highest priority. We implement appropriate and sophisticated technical and physical controls, including encryption at rest and in transit. This is designed to prevent unauthorized access to, or disclosure of, your data and ensure that our use complies with our commitments to you. See Data Privacy FAQs for more information.

When you provide code that you own into AWS Transform, you retain ownership in the ported version of your code. Once the porting is completed, you can review the output and either modify it prior to deploying into production or use it as-is.

Yes. Contact your AWS account team and ask for the Service Accelerator Document for AWS Transform. If your organization has a mutually signed NDA with AWS, then your account team will share the documents.

Unless explicitly opted out, content from AWS Transform might also be used for enhancing or improving the quality of Foundation Models (FMs). This data will not be shared with other third-party model providers. Your content will not be used if you use the opt-out mechanism described in the documentation. For more information, see Sharing your data with AWS.