Amazon Bedrock Launches New Capabilities as Tens of Thousands of Customers Choose It as the Foundation to Build and Scale Secure Generative AI Applications
New Custom Model Import capability lets customers easily bring their proprietary models to Amazon Bedrock so they can take advantage of its powerful capabilities
New Model Evaluation capability makes it easier and faster for customers to take advantage of the widest selection of fully managed models including the new RAG-optimized Titan Embeddings V2 and the latest models from Cohere and Meta
Guardrails for Amazon Bedrock provides customers with best-in-class technology to help them effectively implement safeguards tailored to their application needs and aligned with their responsible AI policies
Tens of thousands of customers and partners, including adidas, ADP, Aha!,
Organizations across all industries, from the world’s fastest growing startups to the most security-conscious enterprises and government institutions, are using Amazon Bedrock to spark innovation, increase productivity, and create new end-user experiences.
“Amazon Bedrock is experiencing explosive growth, with tens of thousands of organizations of all sizes and across all industries choosing it as the foundation for their generative AI strategy because they can use it to move from experimentation to production more quickly and easily than anywhere else,” said Dr.
New Custom Model Import capability helps organizations bring their own customized models to Amazon Bedrock, reducing operational overhead and accelerating application development
In addition to having access to the world’s most powerful models on Amazon Bedrock–from
With Amazon Bedrock Custom Model Import, organizations can now import and access their own custom models as a fully managed application programming interface (API) in Amazon Bedrock, giving them unprecedented choice when building generative AI applications. In just a few clicks, customers can take models that they customized on Amazon SageMaker, or other tools, and easily add them to Amazon Bedrock. Once through an automated validation process, they can seamlessly access their custom model, like any other on Amazon Bedrock, getting all the same benefits that they get today–including seamless scalability and powerful capabilities to safeguard their applications, adhering to responsible AI principles, the ability to expand a model’s knowledge base with retrieval augmented generation (RAG), easily creating agents to complete multi-step tasks, and carrying out fine-tuning to keep teaching and refining models–without needing to manage the underlying infrastructure. With this new capability, AWS makes it easy for organizations to choose a combination of Amazon Bedrock models and their own custom models via the same API. Today, Amazon Bedrock Custom Model Import is available in preview and supports three of the most popular open model architectures, Flan-T5, Llama, and Mistral and with plans for more in the future.
Model Evaluation helps customers assess, compare, and select the best model for their application
With the broadest range of industry-leading models, Amazon Bedrock helps organizations to meet any price, performance, or capability requirements they may have and allows them to run models on their own or in combination with others. However, choosing the best model for a specific use case requires customers to strike a delicate balance between accuracy and performance. Until now, organizations needed to spend countless hours analyzing how every new model can meet their use case, limiting how quickly they could deliver transformative generative AI experiences to their end users. Now generally available, Model Evaluation is the fastest way for organizations to analyze and compare models on Amazon Bedrock, reducing time from weeks to hours spent evaluating models so they can bring new applications and experiences to market faster. Customers can get started quickly by selecting predefined evaluation criteria (e.g., accuracy and robustness) and uploading their own dataset or prompt library, or by selecting from built-in, publicly available resources. For subjective criteria or content requiring nuanced judgment, Amazon Bedrock makes it easy for customers to add humans into the workflow to evaluate model responses based on use-case specific metrics (e.g., relevance, style, and brand voice). Once the setup process is finished, Amazon Bedrock runs evaluations and generates a report so customers can easily understand how the model performed across their key criteria and quickly select the best models for their use cases.
With Guardrails for Amazon Bedrock, customers can use best in class technology to easily implement safeguards to remove personal and sensitive information, profanity, specific words, as well as block harmful content
For generative AI to be pervasive across every industry, organizations need to implement it in a safe, trustworthy, and responsible way. Many models use built-in controls to filter undesirable and harmful content, but most customers want to further tailor their generative AI applications so responses remain relevant, align with company policies, and adhere to responsible AI principles. Now generally available, Guardrails for Amazon Bedrock offers industry-leading safety protection on top of the native capabilities of FMs, helping customers block up to 85% of harmful content. Guardrails is the only solution offered by a top cloud provider that allows customers to have built-in and custom safeguards in a single offering, and it works with all large language models (LLMs) in Amazon Bedrock, as well as fine-tuned models. To create a guardrail, customers simply provide a natural-language description defining the denied topics within the context of their application. Customers can also configure thresholds to filter across areas like hate speech, insults, sexualized language, prompt injection, and violence, as well as filters to remove any personal and sensitive information, profanity, or specific blocked words. Guardrails for Amazon Bedrock empowers customers to innovate quickly and safely by providing a consistent user experience and standardizing safety and privacy controls across generative AI applications.
More model choice: introducing Amazon Titan Text Embeddings V2, the general availability of Titan Image Generator, and the latest models from Cohere and Meta
Exclusive to Amazon Bedrock, Amazon Titan models are created and pre-trained by AWS on large and diverse datasets for a variety of use cases, with built-in support for the responsible use of AI. Today, Amazon Bedrock continues to grow the Amazon Titan family, giving customers even greater choice and flexibility. Amazon Titan Text Embeddings V2, which is optimized for working with RAG use cases, is well suited for a variety of tasks such as information retrieval, question and answer chatbots, and personalized recommendations. To augment FM responses with additional data, many organizations turn to RAG, a popular model-customization technique where the FM connects to a knowledge source that it can reference to augment its responses. However, running these operations can be compute and storage intensive. The new Amazon Titan Text Embeddings V2 model, launching next week, reduces storage and compute costs, all while increasing accuracy. It does so by allowing flexible embeddings to customers, which reduces overall storage up to 4x, significantly reducing operational costs, while retaining 97% of the accuracy for RAG use cases, out-performing other leading models.
Now, generally available, Amazon Titan Image Generator helps customers in industries like advertising, ecommerce, and media and entertainment produce studio-quality images or enhance and edit existing images, at low cost, using natural language prompts. Amazon Titan Image Generator also applies an invisible watermark to all images it creates, helping identify AI-generated images to promote the safe, secure, and transparent development of AI technology and helping reduce the spread of disinformation. The model can also check for the existence of watermark, helping customers confirm whether an image was generated by Amazon Titan Image Generator.
Also available today on Amazon Bedrock are
What Amazon Bedrock customers and partners are saying
Built by Amazon, Rufus is a generative AI-powered expert shopping assistant trained on the company’s extensive product catalog, customer reviews, community Q&As, and information from across the web to answer customer questions on a variety of shopping needs and products, provide comparisons, and make recommendations based on conversational context. “To offer a superior conversational shopping experience to Amazon Stores customers, we worked to develop Rufus into one of the most advanced models ever created by Amazon, and one we knew would benefit our customers far beyond this initial application,” said Trishul Chilimbi, vice president and distinguished scientist, Stores Foundational AI at Amazon. “With Amazon Bedrock Custom Model Import, we are now able to bring Rufus’s advanced underlying model to internal Amazon developers, via Amazon Bedrock, allowing even more builders across our organization to access it as a fully managed API. Now, teams in businesses as diverse as Logistics and Studios are able to build with this model while benefiting from Amazon Bedrock’s streamlined development experience, accelerating the creation of new experiences for all of our customers across Amazon.”
Aha! is a software company that helps more than 1 million people bring their product strategy to life. “Our customers depend on us every day to set goals, collect customer feedback, and create visual roadmaps,” said Dr.
Dentsu is one of the world's largest providers of integrated marketing and technology. “Over the past three months, we’ve been using the Amazon Titan Image Generator model in preview to generate realistic, studio-quality images in large volumes, using natural language prompts, specifically for product placement and brand aligned image generation,” said
Pearson is a leading learning company, serving customers in nearly 200 countries with digital content, assessments, qualifications, and data. “We added Amazon Titan Image Generator to our content management platform because of the quality of the model along with the powerful security and indemnity protection it offers,” said
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