Amazon Bedrock FAQs changed AWS, Jul 6, 2024

1. +Added: Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) along with a broad set of capabilities that you need to build generative AI applications, simplifying development with security, privacy, and responsible AI. With the comprehensive capabilities of Amazon Bedrock, you can experiment with a variety of top FMs, customize them privately with your data using techniques such as fine-tuning and retrieval-augmented generation (RAG), and create managed agents that execute complex business tasks-from booking travel and processing insurance claims to creating ad campaigns and managing inventory-all without writing any code. Since Amazon Bedrock is serverless, you don't have to manage any infrastructure, and you can securely integrate and deploy generative AI capabilities into your applications using the AWS services you are already familiar with.
2. -Removed: Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) along with a broad set of capabilities that you need to build generative AI applications, simplifying development with security, privacy and responsible AI . With Amazon Bedrock's comprehensive capabilities, you can easily experiment with a variety of top FMs, customize them privately with your data using techniques such as fine tuning and retrieval-augmented generation (RAG), and create managed agents that execute complex business tasks-from booking travel and processing insurance claims to creating ad campaigns and managing inventory-all without writing any code. Since Amazon Bedrock is serverless, you don't have to manage any infrastructure, and you can securely integrate and deploy generative AI capabilities into your applications using the AWS services you are already familiar with.
3. +Added: Choice of leading FMs: Amazon Bedrock offers an easy-to-use developer experience to work with a broad range of high-performing FMs from Amazon and leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, and Stability AI. You can quickly experiment with a variety of FMs in the playground, and use a single API for inference regardless of the models you choose, giving you the flexibility to use FMs from different providers and keep up to date with the latest model versions with minimal code changes.
4. -Removed: Choice of leading foundation models: Amazon Bedrock offers an easy-to-use developer experience to work with a broad range of high-performing FMs from Amazon and leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, and Stability AI. You can quickly experiment with a variety of FMs in the playground, and use a single API for inference regardless of the models you choose, giving you the flexibility to use FMs from different providers and keep up to date with the latest model versions with minimal code changes.
5. +Added: Fully managed agents that can invoke APIs dynamically to execute tasks: Build agents that execute complex business tasks-from booking travel and processing insurance claims to creating ad campaigns, preparing tax filings, and managing your inventory-by dynamically calling your company systems and APIs. Fully managed agents for Amazon Bedrock extend the reasoning capabilities of FMs to break down tasks, create an orchestration plan, and execute it.
6. -Removed: Fully managed agents that can invoke APIs dynamically to execute tasks: Build agents that execute complex business tasks-from booking travel and processing insurance claims to creating ad campaigns, preparing tax filings, and managing your inventory-by dynamically calling your company systems and APIs. Amazon Bedrock's fully managed agents extend the reasoning capabilities of FMs to break down tasks, create an orchestration plan, and execute it.
7. +Added: Data security and compliance certifications: Amazon Bedrock offers several capabilities to support security and privacy requirements. Amazon Bedrock is in scope for common compliance standards such as Service and Organization Control (SOC), International Organization for Standardization (ISO), is Health Insurance Portability and Accountability Act (HIPAA) eligible, and customers can use Amazon Bedrock in compliance with the General Data Protection Regulation (GDPR). Amazon Bedrock is CSA Security Trust Assurance and Risk (STAR) Level 2 certified, which validates the use of best practices and the security posture of AWS cloud offerings. With Amazon Bedrock, your content is not used to improve the base models and is not shared with any model providers. Your data in Amazon Bedrock is always encrypted in transit and at rest, and you can optionally encrypt the data using your own keys. You can use AWS PrivateLink with Amazon Bedrock to establish private connectivity between your FMs and your Amazon Virtual Private Cloud (Amazon VPC) without exposing your traffic to the Internet.
8. -Removed: Data security and compliance certifications: Amazon Bedrock offers several capabilities to support security and privacy requirements. Bedrock is in scope for common compliance standards such as Service and Organization Control (SOC), International Organization for Standardization (ISO), Health Insurance Portability and Accountability Act (HIPAA) eligible, and customers can use Bedrock in compliance with the General Data Protection Regulation (GDPR). Amazon Bedrock is CSA Security Trust Assurance and Risk (STAR) Level 2 certified, which validates the use of best practices and the security posture of AWS cloud offerings. With Amazon Bedrock, your content is not used to improve the base models and is not shared with any model providers. Your data in Amazon Bedrock is always encrypted in transit and at rest, and you can optionally encrypt the data using your own keys. You can use AWS PrivateLink with Amazon Bedrock to establish private connectivity between your FMs and your Amazon Virtual Private Cloud (Amazon VPC) without exposing your traffic to the Internet.
9. +Added: With the serverless experience of Amazon Bedrock, you can quickly get started. Navigate to Amazon Bedrock in the AWS Management Console and try out the FMs in the playground. You can also create an agent and test it in the console. Once you've identified your use case, you can easily integrate the FMs into your applications using AWS tools without having to manage any infrastructure.
10. -Removed: With the serverless experience of Amazon Bedrock, you can quickly get started. Navigate to Amazon Bedrock in the AWS console and try out the FMs in the playground. You can also create an agent and test it in the console. Once you've identified your use case, you can easily integrate the FMs into your applications using AWS tools without having to manage any infrastructure.
11. +Added: Amazon Bedrock works with AWS Lambda for invoking actions, Amazon S3 for training and validation data, and Amazon CloudWatch for tracking metrics.
12. -Removed: Amazon Bedrock leverages AWS Lambda for invoking actions, Amazon S3 for training and validation data, and Amazon CloudWatch for tracking metrics.
13. +Added: You can quickly get started with use cases:
14. +Added: Explore more generative AI use cases .
15. -Removed: Explore more generative AI use cases here .
16. +Added: Amazon Bedrock offers a playground that allows you to experiment with various FMs using a conversational chat interface. You can provide a prompt and use a web interface inside the console to supply a prompt and use the pretrained models to generate text or images, or alternatively use a fine-tuned model that has been adapted for your use case.
17. -Removed: Amazon Bedrock offers a playground that allows you to experiment with various FMs using a conversational chat interface. You can provide a prompt and use a web interface inside the AWS Management Console to supply a prompt and use the pretrained models to generate text or images, or alternatively use a fine-tuned model that has been adapted for your use case.
18. +Added: Amazon Bedrock is a managed service that you can use to access FMs. You can fine-tune a model and use it with the Amazon Bedrock API.
19. -Removed: Amazon Bedrock is a managed service that you can use to access foundational models. You can fine-tune a model and use it with the Amazon Bedrock API.
20. +Added: Agents for Amazon Bedrock are fully managed capabilities that make it easier for developers to create generative AI-based applications that can complete complex tasks for a wide range of use cases and deliver up-to-date answers based on proprietary knowledge sources. In just a few short steps, Agents for Amazon Bedrock automatically break down tasks and create an orchestration plan-without any manual coding. The agent securely connects to company data through an API, automatically converting data into a machine-readable format, and augmenting the request with relevant information to generate the most accurate response. Agents can then automatically call APIs to fulfill a user's request. For example, a manufacturing company might want to develop a generative AI application that automates tracking inventory levels, sales data, supply chain information and that can recommend optimal reorder points and quantities to maximize efficiency. As fully managed capabilities, Agents for Amazon Bedrock remove the undifferentiated lifting of managing system integration and infrastructure provisioning, allowing developers to use generative AI to its full extent throughout their organization.
21. -Removed: Agents for Amazon Bedrock are fully managed capabilities that make it easier for developers to create generative AI-based applications that can complete complex tasks for a wide range of use cases and deliver up-to-date answers based on proprietary knowledge sources. With just a few clicks, Agents for Amazon Bedrock automatically break down tasks and create an orchestration plan-without any manual coding. The agent securely connects to company data through an API, automatically converting data into a machine-readable format, and augmenting the request with relevant information to generate the most accurate response. Agents can then automatically call APIs to fulfill a user's request. For example, a manufacturing company might want to develop a generative AI application that automates tracking inventory levels, sales data, supply chain information and can recommend optimal reorder points and quantities to maximize efficiency. As fully managed capabilities, Agents for Amazon Bedrock remove the undifferentiated lifting of managing system integration and infrastructure provisioning, allowing developers to use generative AI to its full extent throughout their organization.
22. +Added: With agents, developers have seamless support for monitoring, encryption, user permissions, and API invocation management without writing custom code. Agents for Amazon Bedrock automate the prompt engineering and orchestration of user-requested tasks. Developers can use the agent-created prompt template as a baseline to further refine it for an enhanced user experience. They can update the user input, orchestration plan, and the FM response. With access to the prompt template developers have better control over the Agent orchestration.
23. -Removed: With agents, developers have seamless support for monitoring, encryption, user permissions, and API invocation management without writing custom code. Agents for Amazon Bedrock automate the prompt engineering and orchestration of user-requested tasks. Developers can use the agent created prompt template as a baseline to further refine it for an enhanced user experience. They can update the user input, orchestration plan, and the FM response. With access to the prompt template developers have better control over the Agent orchestration.
24. +Added: No. Users' inputs and model outputs are not shared with any model providers.
25. -Removed: No. Users inputs and model outputs are not shared with any model providers.
26. +Added: Amazon Bedrock offers several capabilities to support security and privacy requirements. Bedrock is in scope for common compliance standards such as Service and Organization Control (SOC), International Organization for Standardization (ISO), Health Insurance Portability and Accountability Act (HIPAA) eligibility, and customers can use Bedrock in compliance with the General Data Protection Regulation (GDPR). Amazon Bedrock is included in the scope of the SOC 1, 2, 3 reports, allowing customers to gain insights into our security controls. We demonstrate compliance through extensive third-party audits of our AWS controls. Amazon Bedrock is one of the AWS services under ISO Compliance for the ISO 9001, ISO 27001, ISO 27017, ISO 27018, ISO 27701, ISO 22301, and ISO 20000 standards. Amazon Bedrock is CSA Security Trust Assurance and Risk (STAR) Level 2 certified, which validates the use of best practices and the security posture of AWS cloud offerings. With Amazon Bedrock, your content is not used to improve the base models and is not shared with any model providers. You can use AWS PrivateLink to establish private connectivity from Amazon VPC to Amazon Bedrock, without having to expose your data to internet traffic.
27. -Removed: Amazon Bedrock offers several capabilities to support security and privacy requirements. Bedrock is in scope for common compliance standards such as Service and Organization Control (SOC), International Organization for Standardization (ISO), Health Insurance Portability and Accountability Act (HIPAA) eligible, and customers can use Bedrock in compliance with the General Data Protection Regulation (GDPR). Amazon Bedrock is included in the scope of the SOC 1, 2, 3 reports, allowing customers to gain insights into our security controls. We demonstrate compliance through extensive third-party audits of our AWS controls. Amazon Bedrock is one of the AWS services under ISO Compliance for the ISO 9001, ISO 27001, ISO 27017, ISO 27018, ISO 27701, ISO 22301, and ISO 20000 standards. Amazon Bedrock is CSA Security Trust Assurance and Risk (STAR) Level 2 certified, which validates the use of best practices and the security posture of AWS cloud offerings. With Amazon Bedrock, your content is not used to improve the base models and is not shared with any model providers. You can use AWS PrivateLink to establish private connectivity from your Amazon Virtual Private Cloud (VPC) to Amazon Bedrock, without having to expose your data to internet traffic.
28. +Added: No, AWS and the third-party model providers will not use any inputs to or outputs from Amazon Bedrock to train Amazon Titan or any third-party models.
29. -Removed: No, AWS and the third-party model providers will not use any inputs to or outputs from Bedrock to train Amazon Titan or any third-party models.
30. +Added: Amazon Bedrock supports SDKs for runtime services. iOS and Android SDKs, as well as Java, JS, Python, CLI, .Net, Ruby, PHP, Go, and C++, support both text and speech input.
31. -Removed: Amazon Bedrock supports SDKs for runtime services. iOS and Android SDKs, as well as Java, JS, Python, CLI, .Net, Ruby, PHP, Go, and CPP support both text and speech input.
32. +Added: See Amazon Bedrock pricing for current pricing information.
33. -Removed: Please see the Amazon Bedrock Pricing Page for current pricing information.
34. +Added: Depending on your AWS Support contract, Amazon Bedrock is supported under Developer Support, Business Support and Enterprise Support plans.
35. -Removed: Depending on your AWS support contract, Amazon Bedrock is supported under Developer Support, Business Support and Enterprise Support plans.
36. +Added: With Amazon Bedrock, you can privately customize FMs, retaining control over how your data is used and encrypted. Amazon Bedrock makes a separate copy of the base FM and trains this private copy of the model. Your data including prompts, information used to supplement a prompt, and FM responses. Customized FMs remain in the Region where the API call is processed.
37. -Removed: With Amazon Bedrock, you can privately customize FMs, retaining control over how your data is used and encrypted. Amazon Bedrock makes a separate copy of the base foundational model and trains this private copy of the model. Your data including prompts, information used to supplement a prompt, FM responses, and customized FMs remain in the Region where the API call is processed.
38. +Added: When you're fine-tuning a model, your data is never exposed to the public internet, never leaves the AWS network, is securely transferred through your VPC, and is encrypted in transit and at rest. Amazon Bedrock also enforces the same AWS access controls that you have with any of our other services.
39. +Added: Does Amazon Bedrock support continued pretraining?
40. +Added: We launched continued pretraining for Amazon Titan Text Express and Amazon Titan models on Amazon Bedrock. Continued pretraining allows you to continue the pretraining on an Amazon Titan base model using large amounts of unlabeled data. This type of training will adapt the model from a general domain corpus to a more specific domain corpus such as medical, law, finance, and so on, while still preserving most of the capabilities of the Amazon Titan base model.
41. +Added: Why should I use continued pretraining in Amazon Bedrock?
42. +Added: Enterprises may want to build models for tasks in a specific domain. The base models may not be trained on the technical jargon used in that specific domain. Thus, directly fine-tuning the base model requires large amounts of labeled training records and a long training duration to get accurate results. To ease this burden, the customer can instead provide large amounts of unlabeled data for a continued pretraining job. This job will adapt the Amazon Titan base model to the new domain. Then the customer may fine-tune the newly pretrained custom model to downstream tasks, using significantly fewer labeled training records and with a shorter training duration.
43. +Added: How does the continued pretraining feature relate to other AWS services?
44. +Added: Amazon Bedrock continued pretraining and fine-tuning have very similar requirements. For this reason, we are choosing to create unified APIs that support both continued pretraining and fine-tuning. Unification of the APIs reduces the learning curve and will help customers use standard features such as Amazon EventBridge to track long running jobs, Amazon S3 integration for fetching training data, resource tags, and model encryption.
45. -Removed: When you're fine tuning a model, your data is never exposed to the public internet, never leaves the AWS network, is securely transferred through your VPC, and is encrypted in transit and at rest. And, Bedrock enforces the same AWS access controls that you have with any of our other services.
46. -Removed: Does Amazon Bedrock support Continued Pre-training?
47. -Removed: We launched Continued Pre-training for Titan Text Express and Titan models on Amazon Bedrock; this will enable you to continue the pre-training on a Titan base model using large amounts of unlabeled data. This type of training will adapt the model from a general domain corpus to a more specific domain corpus such as medical, law, finance, etc. while still preserving most of the capabilities of the Titan base model.
48. -Removed: Why should I use Continued Pre-training in Bedrock?
49. -Removed: Typically, enterprises may want to build models for tasks in a specific domain. The base models may not be trained on the technical jargon used in that specific domain. Thus, fine-tuning the base model directly will require large amounts of labeled training records and a long training duration to get accurate results. To ease this burden, the customer can instead provide large amounts of unlabeled data for a Continued Pre-Training job. This job will adapt the Titan base model to the new domain. Then the customer may fine tune the newly pre-trained custom model to downstream tasks using significantly less labeled training records and with less training duration.
50. -Removed: How does the Continued Pre-training feature relate to other AWS services?
51. -Removed: Bedrock Continued Pre-training and Fine Tuning (FT) have very similar requirements. For this reason, we are choosing to create unified APIs that supports both CPT and FT. Unification of the APIs reduces the learning curve and will help customers use standard features such as CloudWatch Event Bridge to track long running jobs, S3 integration for fetching training data, Resource tags and Model encryption.
52. +Added: Continued pretraining helps you adapt the Amazon Titan models to your domain specific data while still preserving the base functionality of the Amazon Titan models. To create a continued pretraining job, navigate to the Amazon Bedrock console and click on "Custom Models." You will navigate to the custom model page that has two tabs: Models and Training jobs. Both tabs provide a "Customize Model" drop-down menu on the right. Select "Continued Pretraining" from the drop-down menu to navigate to "Create Continued Pretraining Job." You will provide the source model, name, model encryption, input data, hyper-parameters and output data. Additionally, you can provide tags, along with details about AWS Identity and Access Management (IAM) roles and resource policies for the job.
53. +Added: Exclusive to Amazon Bedrock, the Amazon Titan family of models incorporates 25 years of Amazon experience innovating with AI and machine learning across the business. Amazon Titan FMs provide customers with a breadth of high-performing image, multimodal, and text model choices through a fully managed API. Amazon Titan models are created by AWS and pretrained on large datasets, making them powerful, general-purpose models built to support a variety of use cases, while also supporting the responsible use of AI. Use them as is or privately customize them with your own data.
54. +Added: Where can I learn more about the data processed to develop and train Amazon Titan FMs?
55. +Added: To learn more about data processed to develop and train Amazon Titan FMs, visit Amazon Titan model training and privacy .
56. -Removed: Continued Pre-training helps you easily adapt the Titan models to your domain specific data while still preserving base functionality of the Titan models. To create a Continued Pre-training job, navigate to the Bedrock Console and click on 'Custom Models'. You will navigate to the custom model page that has two tabs: Models and Training jobs. Both tabs provide a drop-down on the right called as "Customize Model". Select "Continued Pre-training" from the Customize Model drop-down to navigate to the "Create Continued pre-training job " screen. You will provide the source model, name, model encryption, input data, hyper-parameters and output data. Additionally, you can provide Tags along with details about IAM roles and resource policies for the job.
57. -Removed: Exclusive to Amazon Bedrock, the Amazon Titan family of models incorporates Amazon's 25 years of experience innovating with AI and machine learning across its business. Amazon Titan foundation models (FMs) provide customers with a breadth of high-performing image, multimodal, and text model choices, via a fully managed API. Amazon Titan models are created by AWS and pretrained on large datasets, making them powerful, general-purpose models built to support a variety of use cases, while also supporting the responsible use of AI. Use them as is or privately customize them with your own data.
58. -Removed: Where can I learn more about data processed to develop and train Amazon Titan FMs?
59. -Removed: To learn more about data processed to develop and train Amazon Titan FMs, visit the Amazon Titan Model Training & Privacy page .
60. +Added: Supported data formats include .pdf, .txt, .md, .html, .doc and .docx, .csv, .xls, and .xlsx files. Files must be uploaded to Amazon S3. Point to the location of your data in Amazon S3, and Knowledge Bases for Amazon Bedrock takes care of the entire ingestion workflow into your vector database.

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