The latest models from Anthropic, Cohere, Meta,
Stability AI, and Amazon expand customers’ choice of
industry-leading models to support a variety of use cases
Model Evaluation on Amazon Bedrock helps
customers evaluate, compare, and select the best model for their
use case and business needs
Knowledge Bases for Amazon Bedrock makes it
even easier to build generative AI applications that use
proprietary data to deliver customized, up-to-date responses
Customers have more options to customize models
in Amazon Bedrock with fine-tuning support for Cohere Command, Meta
Llama 2, and Amazon Titan models, with Anthropic Claude coming
soon
With Agents for Amazon Bedrock, customers can
enable generative AI applications to plan and perform a wide
variety of multistep business tasks securely and privately
Guardrails for Amazon Bedrock helps customers
implement safeguards customized to their generative AI applications
and aligned with their responsible AI policies
Blueshift, dentsu, Druva, GoDaddy, INRIX,
MongoDB, OfferUp, Salesforce, SmartBots AI, and TTEC Digital are
among the customers and partners using Amazon Bedrock to harness
generative AI
At AWS re:Invent, Amazon Web Services, Inc. (AWS), an
Amazon.com, Inc. company (NASDAQ: AMZN), today announced Amazon
Bedrock innovations that expand model choice and deliver powerful
capabilities, making it easier for customers to build and scale
generative artificial intelligence (AI) applications customized to
their businesses. Amazon Bedrock is a fully managed service that
offers easy access to a choice of industry-leading large language
models and other foundation models from AI21 Labs, Anthropic,
Cohere, Meta, Stability AI, and Amazon, along with a broad set of
capabilities that customers need to build generative AI
applications—simplifying development while supporting privacy and
security. These announcements further democratize access to
generative AI by empowering customers with even more choice of
industry-leading models and new capabilities to evaluate them,
simplifying how they customize models with relevant and proprietary
data, supplying tools to automate the execution of complex tasks,
and equipping customers with safeguards to build and deploy
applications responsibly. Together, these new additions to Amazon
Bedrock transform how organizations of all sizes and across all
industries can use generative AI to spark innovation and reinvent
customer experiences. To get started with Amazon Bedrock, visit
aws.amazon.com/bedrock.
“Generative AI is poised to be the most transformational
technology of our time, and we are inspired by how customers are
applying it to new opportunities and tackling business challenges,”
said Dr. Swami Sivasubramanian, vice president of Data and AI at
AWS. “As customers incorporate generative AI into their businesses,
they turn to Amazon Bedrock for its choice of leading models,
customization features, agent capabilities, and enterprise-grade
security and privacy in a fully managed experience. With even more
tools at their fingertips, customers are using Amazon Bedrock to
leverage the full potential of generative AI to reimagine user
experiences, reinvent their businesses, and accelerate their
generative AI journeys.”
Organizations want to use generative AI for a wide variety of
use cases—like generating productivity gains, driving innovative
user experiences, and reimagining work—but generative AI is
evolving rapidly, with new options and innovations happening daily.
With so much fluidity in this space, customers’ ability to adapt is
arguably the most valuable tool of all. Organizations need to be
able to experiment, deploy, iterate, and pivot using the latest and
greatest models available, and be ready to immediately embrace what
comes tomorrow. To address these challenges, AWS developed Amazon
Bedrock to make building with—and moving between—a range of models
as easy as an API call, to put the latest techniques for model
customization in the hands of all developers, and to keep customers
secure and their data private. This is why customers such as Alida,
Automation Anywhere, Blueshift, BMW Group, Clariant, Coinbase, Cox
Automotive, dentsu, Druva, Genesys, Gilead, GoDaddy, Hellmann
Worldwide Logistics, INRIX, KONE, LexisNexis Legal &
Professional, Lonely Planet, NatWest, Nexxiot, OfferUp, Omnicom,
the PGA TOUR, Proofpoint, Salesforce, Siemens, Takenaka
Corporation, and Verint have turned to Amazon Bedrock to help them
harness the power of generative AI for their organizations. Today’s
announcement introduces new models and capabilities that will make
it even easier for customers to build and scale generative AI
applications.
The latest models from Anthropic, Cohere, Meta, and Stability
AI, as well as additions to the Amazon Titan family, expand model
choice for customers
No single model is ideal for every use case. Models vary across
capabilities, price, and performance. Customers need easy access to
a variety of model choices, so they can try out different models,
switch between them, and combine the best models for their needs.
With Amazon Bedrock, customers can drive rapid innovation with the
latest versions of models, including the newly available Anthropic
Claude 2.1 and Meta Llama 2 70B, and the recently available Cohere
Command Light, Cohere Embed English, Cohere Embed multilingual,
Meta Llama 2 13B, and Stability AI Stable Diffusion XL 1.0—all
accessible via an API. In addition to Amazon Titan Text Embeddings
and Amazon Titan Text models (now generally available), AWS is
introducing Amazon Titan Image Generator and Amazon Titan
Multimodal Embeddings to give customers even more choice and
flexibility to build generative AI applications with models.
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. And
Amazon indemnifies customers against claims that generally
available Amazon Titan models or their outputs infringe on
third-party copyrights.
- Anthropic’s Claude 2.1 in Amazon Bedrock: Anthropic, an
AI safety and research company that builds reliable, interpretable,
and steerable AI systems, has brought Claude 2.1, the latest
version of their language model, to Amazon Bedrock. Claude 2.1
offers a 200K token context window and improved accuracy over long
documents. Customers can now process text heavy documents like
financial statements and internal datasets, and Claude 2.1 can
summarize, perform Q&A, or contrast documents, and much more.
Anthropic reports that Claude 2.1 has made significant gains in
honesty with a 2x decrease in false statements compared to their
previous model.
- Meta Llama 2 70B in Amazon Bedrock: Llama 2 is the next
generation of language models by Meta. Llama 2 was trained on 40%
more data than Llama 1 and has double the context length. The Llama
2 70 billion-parameter model is now available in Amazon Bedrock, in
addition to the recently announced Llama 2 13 billion-parameter
model. Built on top of the pre-trained Llama model, Llama 2 is
optimized for dialog use cases through fine-tuning with instruction
datasets and more than 1 million human annotations. The models
perform competitively against multiple external benchmarks,
including reasoning, coding, proficiency, and knowledge tests, and
offer a compelling combination of price and performance in Amazon
Bedrock.
- New Amazon Titan Image Generator available in preview:
Amazon Titan Image Generator helps customers in industries like
advertising, ecommerce, and media and entertainment produce
studio-quality, realistic images or enhance existing images using
natural language prompts, for rapid ideation and iteration on large
volumes of images and at low cost. The model can understand complex
prompts and generate relevant images with accurate object
composition and limited distortions, reducing the generation of
harmful content and mitigating the spread of misinformation.
Customers can use the model in the Amazon Bedrock console either by
submitting a natural language prompt to generate an image or by
uploading an image for automatic editing, before configuring the
dimensions and specifying the number of variations the model should
generate. To edit, customers can isolate parts of an image to add
or replace details (e.g., inserting a surfboard into a beach scene
or replacing mountains with a forest in the background of a car
advertisement), or they can extend an image’s borders with
additional details in the same style as the original. Building on
the commitments AWS made earlier this year at the White House,
Amazon Titan applies an invisible watermark to all images it
generates to help reduce the spread of misinformation by providing
a discreet mechanism to identify AI-generated images and to promote
the safe, secure, and transparent development of AI technology. AWS
is among the first model providers to widely release built-in
invisible watermarks that are integrated into the image outputs and
are designed to be resistant to alterations.
- New Amazon Titan Multimodal Embeddings generally
available: Amazon Titan Multimodal Embeddings helps customers
power more accurate and contextually relevant multimodal search and
recommendation experiences for end users. The model converts images
and short text into embeddings—numerical representations that allow
the model to easily understand semantic meanings and relationships
among data— which are stored in a customer’s vector database. End
users can submit search queries using any combination of image and
text prompts. The model will generate embeddings for the search
query and match them to the stored embeddings to produce more
accurate and relevant search and recommendation results for end
users. For example, a stock photography company with hundreds of
millions of images can use the model to power its search
functionality, so users can search for images using a phrase,
image, or a combination of image and text (e.g., “show me images
similar to the provided image, but with sunny skies”). By default,
the model generates vectors that are well suited for search
experiences that require a high degree of accuracy and speed.
However, customers can also generate smaller dimensions to optimize
for speed and performance. Amazon Titan Multimodal Embeddings joins
the existing Amazon Titan Text Embeddings model, which is used to
convert text input like single words, phrases, or even large
documents into embeddings for use cases like search and
personalization.
New capability helps customers efficiently evaluate, compare,
and select the best model for their use case and business
needs
Today, organizations have a wide range of model options to power
their generative AI applications. To strike the right balance of
accuracy and performance for their use case, organizations must
efficiently compare models and find the best option based on their
preferred metrics. To compare models, organizations must first
spend days identifying benchmarks, setting up evaluation tools, and
running assessments, all of which requires deep expertise in data
science. Furthermore, these tests are not useful for evaluating
subjective criteria (e.g., brand voice, relevance, and style) that
requires judgment through tedious, time-intensive, human-review
workflows. The time, expertise, and resources required for these
comparisons—for every new use case—make it difficult for
organizations to choose the optimal model for a task, limiting
their use of generative AI.
Now available in preview, Model Evaluation on Amazon Bedrock
helps customers evaluate, compare, and select the best models for
their specific use case, using either automatic or human
evaluations. In the Amazon Bedrock console, customers choose the
models they want to compare for a given task, such as
question-answering or content summarization. For automatic
evaluations, customers select predefined evaluation criteria (e.g.,
accuracy, robustness, and toxicity) and upload their own testing
dataset or select from built-in, publicly available datasets. For
subjective criteria or nuanced content requiring sophisticated
judgment, customers can easily set up human-based evaluation
workflows with just a few clicks. These workflows leverage a
customer’s in-house workforce, or use a managed workforce provided
by AWS, to evaluate model responses. During human-based
evaluations, customers define use case-specific metrics (e.g.,
relevance, style, and brand voice). Once customers finish the setup
process, Amazon Bedrock runs evaluations and generates a report, so
customers can easily understand how the model performed across key
criteria and can make optimal tradeoffs and quickly select the best
models for their use cases.
New model customization capabilities help customers make the
most of their data, privately and securely, on AWS
Organizations want to maximize the value of their rich data
sources to deliver remarkable user experiences—at scale—that are
uniquely customized to reflect the company’s style, voice, and
services. New, purpose-built capabilities available in Amazon
Bedrock help customers personalize models privately and securely
with their own data to build differentiated generative AI-powered
applications.
- Knowledge Bases for Amazon Bedrock customizes model
responses with contextual and relevant company data:
Organizations want to supplement existing models with proprietary
data to create more relevant and accurate responses. To equip the
model with up-to-date information, organizations turn to retrieval
augmented generation (RAG), a technique that allows customers to
customize a model’s responses by augmenting prompts with data from
multiple sources, including document repositories, databases, and
APIs. Now generally available, Knowledge Bases for Amazon Bedrock
securely connects models to proprietary data sources for RAG to
deliver more accurate, context-specific responses for use cases
like chatbots and question-answering systems. Knowledge bases are
fully managed, so customers simply point to the location of their
data. Then knowledge bases fetch the text documents and save the
data to a vector database or set one up on the customer’s behalf.
When a user query comes in, Amazon Bedrock orchestrates RAG by
fetching text needed to augment a prompt, sending the prompt to the
model, and returning the response. Knowledge Bases for Amazon
Bedrock supports databases with vector capabilities, including
Amazon OpenSearch, and other popular databases like Pinecone and
Redis Enterprise Cloud, with Amazon Aurora and MongoDB coming
soon.
- Cohere Command, Meta Llama 2, and Amazon Titan models can
now be fine-tuned in Amazon Bedrock, with support for Anthropic’s
Claude 2 coming soon: In addition to RAG, organizations can
also leverage fine-tuning to further train the model on a specific
task (e.g., text generation), using labeled datasets to adapt the
model’s parameters to their business, and extending its knowledge
with the lexicon and terminology used by the organization and end
users. For example, a retail customer could fine-tune a model on a
dataset of product descriptions to help it understand the brand
style and produce more accurate descriptions for the website.
Amazon Bedrock now supports fully managed fine-tuning for Cohere
Command and Meta Llama 2, along with Amazon Titan Text Express,
Amazon Titan Text Lite, Amazon Titan Multimodal Embeddings, and
Amazon Titan Image Generator (in preview), so customers can use
labeled datasets to increase model accuracy for specific tasks.
Additionally, AWS customers will soon be able to fine-tune Claude
2’s performance with their data sources. To fine-tune a model,
customers start by selecting the model and using Amazon Bedrock to
make a copy. Customers then point to labeled examples in Amazon
Simple Storage Service (Amazon S3). Amazon Bedrock incrementally
trains the model (augments the copied model with the new
information) on these examples, and the result is a private, more
accurate fine-tuned model that delivers more relevant, customized
responses. Customer data is encrypted in transit and at rest, so
all valuable customer data remains secure and private. AWS and
third-party model providers will not use any inputs or outputs from
Amazon Bedrock to train their base models.
With Agents for Amazon Bedrock, generative AI applications
can help execute multistep tasks using company systems and data
sources
While models are effective at conversing and creating new
content, they deliver more value if equipped to take actions, solve
problems, and interact with a range of systems to complete
multistep tasks (e.g., booking travel or ordering replacement
parts). However, this requires custom integrations to connect
models with company data sources, APIs, and internal and external
systems. Developers must write code to orchestrate the interactions
between models, systems, and the user, so the application can
execute a series of API calls in a logical order. To connect the
model with data sources, developers must implement RAG, so the
model can customize its responses to the task. Finally, developers
must provision and manage the requisite infrastructure, as well as
establish policies for data security and privacy. These steps are
time-consuming and require expertise, slowing the development of
generative AI applications.
Now generally available, fully managed Agents for Amazon Bedrock
enables generative AI applications to execute multistep tasks using
company systems and data sources. Agents can plan and perform most
business tasks, such as answering questions about product
availability or taking orders. Customers can create an agent using
a simple setup process, first selecting the desired model, writing
a few instructions in natural language (e.g., “you are a cheerful
customer service agent” and “check product availability in the
inventory system”) and providing access to the company’s enterprise
systems and knowledge bases. Agents automatically analyze the
request and break it down into a logical sequence, using the
model’s reasoning capabilities to determine the information needed.
The agent then takes action by identifying the APIs to call and
deciding when to call them to fulfill the request. Agents also
retrieve needed information from proprietary data sources to
provide accurate and relevant responses. Agents securely and
privately perform this process in the background each time,
relieving customers from having to engineer prompts, manage the
session context, or orchestrate systems manually. With Agents for
Amazon Bedrock, customers can improve the accuracy and speed of
development of their generative AI applications.
With Guardrails for Amazon Bedrock, customers can implement
safeguards across models based on application requirements and
responsible AI policies
Organizations recognize the need to manage interactions within
generative AI applications for a relevant and safe user experience.
While many models use built-in controls to filter undesirable and
harmful content, organizations want to further customize
interactions to remain on topics relevant to their business, align
with company policies, and adhere to responsible AI principles. For
example, a bank might want to configure its online assistant to
refrain from providing investment advice, avoid queries about
competitors, and limit harmful content. As another example, after a
customer service call, personally identifiable information (PII)
may need to be redacted from the call summary. Organizations may
need to change models, use multiple models, or replicate policies
across applications, and they want a simple way to consistently
deploy their preferences across all these areas simultaneously.
Deep expertise is required to build custom protection systems with
these kinds of safeguards and integrate them into applications, and
the processes can take months. Organizations want a streamlined way
to enforce key policies and rules in generative AI applications to
deliver relevant user experiences and support safer use of the
technology.
Now available in preview, Guardrails for Amazon Bedrock empowers
customers to implement safeguards for generative AI applications
that are customized to their use cases and responsible AI
principles, enhancing the safety and privacy of user interactions.
Guardrails drive consistency in how models in Amazon Bedrock
respond to undesirable and harmful content within applications.
Customers can apply guardrails to all large language models in
Amazon Bedrock, as well as to fine-tuned models and in combination
with Agents for Amazon Bedrock. To create a guardrail in the Amazon
Bedrock console, customers start with natural language descriptions
to define the denied topics within the context of their
application. Customers can also configure thresholds across hate
speech, insults, sexualized language, and violence to filter out
harmful content to their desired level. In early 2024, customers
will also be able to redact PII in models’ responses, set profanity
filters, and provide a list of custom words to block interactions
between users and models. Guardrails automatically evaluate both
user queries and model responses to detect and help prevent content
that falls into restricted categories. Customers can create
multiple guardrails to support different use cases and apply the
same guardrails across multiple models. Guardrails for Amazon
Bedrock empowers customers to innovate safely by providing a
consistent user experience and standardizing safety and privacy
controls across generative AI applications.
Blueshift provides brands with marketing automation and customer
data platforms to deliver personalized, customer engagement across
all communication channels and devices. “Product catalogs are
rapidly evolving with new content being changed every minute, and
we need to continuously update our embeddings to ensure
recommendations for brand audiences remain relevant,” said Manyam
Mallela, co-founder and chief AI officer at Blueshift. “Amazon
Titan Multimodal Embeddings in Amazon Bedrock is outperforming
older models from other providers that we used, offering more
nuanced and contextually relevant recommendations without complex
feature engineering. Our team has seen a 10% performance
improvement using Amazon Titan Multimodal Embeddings. With the
robust infrastructure, security, and collaboration offered by AWS,
Blueshift is poised to seamlessly integrate cutting-edge
embeddings, ensuring that our recommendation solutions remain
state-of-the-art and lead to improved audience engagement.”
Dentsu is one of the world's largest providers of integrated
marketing and technology services. “We work at the convergence of
marketing, technology, and consulting to drive people-centered
transformations for brands that want to shape society for the
better, and generative AI is changing our ability to deliver at
scale and speed for clients, augmenting, not replacing, our
72,000-strong team around the world,” said Brian Klochkoff,
executive vice president of Innovation & Emerging Technologies
at dentsu. “Specifically, Amazon Bedrock gives us the enterprise
control and ease-of-use to deploy third-party models for
decentralized usage across our product and engineering teams. This
allows our teams to innovate with the latest and greatest
generative AI advancements in a safe and responsible space, while
inventing cutting-edge opportunities for clients.”
Druva enables cyber, data, and operational resilience for every
organization with their leading, at-scale software-as-a-service
(SaaS) solution. “It takes time and effort to manually build
architecture that incorporates real-time and regularly changing
data into applications, so we wanted a way to automate this
process,” said David Gildea, vice president of Product, Generative
AI at Druva. “We built our new service Dru—an AI co-pilot that IT
teams can use to access company information and perform actions in
natural language—in Amazon Bedrock because it provides fully
managed and secure access to an array of foundation models. Next,
we plan to integrate agents and knowledge bases into Dru to simply
implement and automate RAG and improve end-user experiences, which
we expect will lead to 70% more accurate responses, 50% faster
response times, and 50% lower costs compared to similar models on
other platforms. With Agents and Knowledge Bases for Amazon
Bedrock, we can add new capabilities, like support ticket creation,
with increased velocity and without having to re-engineer
Dru—enabling us to deliver the latest generative AI-powered
solutions to our customers.”
GoDaddy is a leading domain registrar, commerce, and web hosting
company serving more than 20 million customers. “Everyday
entrepreneurs—from bakers to plumbers—use GoDaddy to establish
their businesses online, and we are heavily leaning into generative
AI as it can help these entrepreneurs compete with and become the
next big brand,” said Travis Muhlestein, chief data and analytics
officer at GoDaddy. “We have long worked with AWS because of their
dedication to customers, history of democratizing cloud services,
and current mission of making generative AI accessible to
businesses of all sizes. Amazon Bedrock plays a critical role in
our AI platform, and we plan to use Amazon Titan Text Express to
accelerate internal development patterns. We are especially excited
to test Amazon Titan Image Generator because it has the potential
to help small businesses create professional marketing assets that
solidify their brand identities and can propel their businesses
exponentially.”
INRIX is a global leader in connected car services and
transportation analytics. “The insights we provide our customers
are informed by a vast amount of data, so it is critical that we
pursue the most efficient and effective generative AI solutions to
drive data analysis through our new causality platform,” said Bryan
Mistele, chief executive officer at INRIX. “Amazon Bedrock's model
evaluation capability simplifies the process of identifying the
right foundation model for our needs by aggregating key metrics,
expediting our selection from days to minutes. For content needing
sophisticated judgement, we can use our own employees at scale to
evaluate the model's responses and ensure they accurately reflect
our domain and the brand voice of our chatbot application. With a
visual and comprehensive resulting report, not only does the
capability boost our confidence that we have selected the right
model for the task, but it helps us focus instead on delivering
innovative services to move people, cities, and businesses
forward.”
MongoDB's mission is to empower innovators to create, transform,
and disrupt industries by unleashing the power of software and
data. “More and more customers across industries want to take
advantage of generative AI to build next-generation applications,
but many of them are concerned about data privacy and ensuring the
accuracy of the outputs from AI-powered systems,” said Sahir Azam,
chief product officer at MongoDB. “To meet customers where they
are, we made MongoDB Atlas available as a knowledge base for Amazon
Bedrock, so our joint customers can securely build generative AI
applications with their operational data to create personalized
experiences with the trust and accuracy their end users expect.
Through this integration, customers can access industry-leading
foundation models and use their data processed by MongoDB Atlas
Vector Search to create applications that deliver more relevant
outputs with the right context. Leveraging the data privacy best
practices built into Knowledge Bases for Amazon Bedrock, customers
can spend less time on the operational overhead of generative AI
and focus more on deploying the technology to provide highly
engaging end-user experiences on AWS.”
OfferUp is one of the largest mobile marketplaces for local
buyers and sellers in the U.S. and is changing the way people
transact in their communities by providing a uniquely simple and
trusted experience. “With millions of listings posted daily, it is
important that we continually improve our personalized search and
recommendation experiences for our users,” said Melissa Binde,
chief technology officer at OfferUp. “To achieve this goal, we are
experimenting with Amazon Titan Multimodal Embeddings, with the aim
of revolutionizing local commerce through cutting-edge semantic
search capabilities. During an initial evaluation with the new
multimodal model, we have observed substantial improvement in
relevance recall for keyword searches. This advancement will
significantly expedite successful matches, benefiting both our
buyers and sellers.”
Salesforce is a leading AI customer relationship management
(CRM) platform, driving productivity and trusted customer
experiences powered by AI, CRM, and data. “AI is an integral part
of our commitment to help companies connect with their customers in
new and personalized ways,” said Kaushal Kurapati, senior vice
president of Product at Salesforce. “Amazon Bedrock is a key
component of our open ecosystem approach to models, seamlessly
grounding models with customer data, and integrating them into
workflows across Salesforce. And with the addition of new
evaluation capabilities, comparing foundation models across
multiple criteria—including qualitative aspects such as
friendliness, style, and brand relevance—will make operationalizing
models easier and faster than ever.”
SmartBots AI helps enterprises build their own conversational
chatbots to deliver elevated customer support and sales
experiences. “It is critical that our customers can trust the
responses of the conversational chatbots they are building with
SmartBots AI, as these responses represent their brands and will be
used by their employees and clients,” said Jaya Prakash Kommu,
co-founder and chief technology officer at SmartBots AI. “Because
our chatbot development service is powered by Amazon Bedrock,
customers’ chatbots inherit AWS data safety and privacy best
practices. Their chatbots will also have access to Guardrails for
Amazon Bedrock to set, control, and avoid inappropriate and
unwanted content in user prompts and responses that their chatbots
generate, so users have safe and brand-adhering experiences. Our
customers are excited for this safety feature to build and use
generative AI responsibly.”
TTEC Digital uses AI to improve customer experiences through
cutting-edge contact center technologies. “Our clients are always
looking for ways to shorten contact center response times for their
customer interactions, so we are building a generative AI-powered
solution using Amazon Bedrock to partially automate information
gathering and response generation,” said Dave Seybold, chief
executive officer at TTEC Digital. “We will soon be deploying
Amazon Titan Text Express within our clients’ enterprise contact
center environments to enable rapid retrieval of customer data
through simple prompting, helping their contact center
representatives provide fast, more informed responses. We predict
this solution will increase customer satisfaction, decrease average
call times, reduce post-call workload for representatives, and
lower labor costs. With Amazon Titan Text Express, our clients’
contact center representatives will be empowered to address a
broader range of customer issues and extend the transformative
advantage of generative AI to their customers.”
About Amazon Web Services
Since 2006, Amazon Web Services has been the world’s most
comprehensive and broadly adopted cloud. AWS has been continually
expanding its services to support virtually any workload, and it
now has more than 240 fully featured services for compute, storage,
databases, networking, analytics, machine learning and artificial
intelligence (AI), Internet of Things (IoT), mobile, security,
hybrid, virtual and augmented reality (VR and AR), media, and
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Availability Zones within 32 geographic regions, with announced
plans for 15 more Availability Zones and five more AWS Regions in
Canada, Germany, Malaysia, New Zealand, and Thailand. Millions of
customers—including the fastest-growing startups, largest
enterprises, and leading government agencies—trust AWS to power
their infrastructure, become more agile, and lower costs. To learn
more about AWS, visit aws.amazon.com.
About Amazon
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than competitor focus, passion for invention, commitment to
operational excellence, and long-term thinking. Amazon strives to
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