Tag Archives: AWS

AWS Weekly Roundup — Claude 3 Haiku in Amazon Bedrock, AWS CloudFormation optimizations, and more — March 18, 2024

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Storage, storage, storage! Last week, we celebrated 18 years of innovation on Amazon Simple Storage Service (Amazon S3) at AWS Pi Day 2024. Amazon S3 mascot Buckets joined the celebrations and had a ton of fun! The 4-hour live stream was packed with puns, pie recipes powered by PartyRock, demos, code, and discussions about generative AI and Amazon S3.

AWS Pi Day 2024

AWS Pi Day 2024 — Twitch live stream on March 14, 2024

In case you missed the live stream, you can watch the recording. We’ll also update the AWS Pi Day 2024 post on community.aws this week with show notes and session clips.

Last week’s launches
Here are some launches that got my attention:

Anthropic’s Claude 3 Haiku model is now available in Amazon Bedrock — Anthropic recently introduced the Claude 3 family of foundation models (FMs), comprising Claude 3 Haiku, Claude 3 Sonnet, and Claude 3 Opus. Claude 3 Haiku, the fastest and most compact model in the family, is now available in Amazon Bedrock. Check out Channy’s post for more details. In addition, my colleague Mike shows how to get started with Haiku in Amazon Bedrock in his video on community.aws.

Up to 40 percent faster stack creation with AWS CloudFormation — AWS CloudFormation now creates stacks up to 40 percent faster and has a new event called CONFIGURATION_COMPLETE. With this event, CloudFormation begins parallel creation of dependent resources within a stack, speeding up the whole process. The new event also gives users more control to shortcut their stack creation process in scenarios where a resource consistency check is unnecessary. To learn more, read this AWS DevOps Blog post.

Amazon SageMaker Canvas extends its model registry integrationSageMaker Canvas has extended its model registry integration to include time series forecasting models and models fine-tuned through SageMaker JumpStart. Users can now register these models to the SageMaker Model Registry with just a click. This enhancement expands the model registry integration to all problem types supported in Canvas, such as regression/classification tabular models and CV/NLP models. It streamlines the deployment of machine learning (ML) models to production environments. Check the Developer Guide for more information.

For a full list of AWS announcements, be sure to keep an eye on the What’s New at AWS page.

Other AWS news
Here are some additional news items, open source projects, and Twitch shows that you might find interesting:

AWS Build On Generative AIBuild On Generative AI — Season 3 of your favorite weekly Twitch show about all things generative AI is in full swing! Streaming every Monday, 9:00 US PT, my colleagues Tiffany and Darko discuss different aspects of generative AI and invite guest speakers to demo their work. In today’s episode, guest Martyn Kilbryde showed how to build a JIRA Agent powered by Amazon Bedrock. Check out show notes and the full list of episodes on community.aws.

Amazon S3 Connector for PyTorch — The Amazon S3 Connector for PyTorch now lets PyTorch Lightning users save model checkpoints directly to Amazon S3. Saving PyTorch Lightning model checkpoints is up to 40 percent faster with the Amazon S3 Connector for PyTorch than writing to Amazon Elastic Compute Cloud (Amazon EC2) instance storage. You can now also save, load, and delete checkpoints directly from PyTorch Lightning training jobs to Amazon S3. Check out the open source project on GitHub.

AWS open source news and updates — My colleague Ricardo writes this weekly open source newsletter in which he highlights new open source projects, tools, and demos from the AWS Community.

Upcoming AWS events
Check your calendars and sign up for these AWS events:

AWS at NVIDIA GTC 2024 — The NVIDIA GTC 2024 developer conference is taking place this week (March 18–21) in San Jose, CA. If you’re around, visit AWS at booth #708 to explore generative AI demos and get inspired by AWS, AWS Partners, and customer experts on the latest offerings in generative AI, robotics, and advanced computing at the in-booth theatre. Check out the AWS sessions and request 1:1 meetings.

AWS SummitsAWS Summits — It’s AWS Summit season again! The first one is Paris (April 3), followed by Amsterdam (April 9), Sydney (April 10–11), London (April 24), Berlin (May 15–16), and Seoul (May 16–17). AWS Summits are a series of free online and in-person events that bring the cloud computing community together to connect, collaborate, and learn about AWS.

AWS re:InforceAWS re:Inforce — Join us for AWS re:Inforce (June 10–12) in Philadelphia, PA. AWS re:Inforce is a learning conference focused on AWS security solutions, cloud security, compliance, and identity. Connect with the AWS teams that build the security tools and meet AWS customers to learn about their security journeys.

You can browse all upcoming in-person and virtual events.

That’s all for this week. Check back next Monday for another Weekly Roundup!

— Antje

This post is part of our Weekly Roundup series. Check back each week for a quick roundup of interesting news and announcements from AWS!

Run and manage open source InfluxDB databases with Amazon Timestream

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Starting today, you can use InfluxDB as a database engine in Amazon Timestream. This support makes it easy for you to run near real-time time-series applications using InfluxDB and open source APIs, including open source Telegraf agents that collect time-series observations.

Now you have two database engines to choose in Timestream: Timestream for LiveAnalytics and Timestream for InfluxDB.

You should use the Timestream for InfluxDB engine if your use cases require near real-time time-series queries or specific features in InfluxDB, such as using Flux queries. Another option is the existing Timestream for LiveAnalytics engine, which is suitable if you need to ingest more than tens of gigabytes of time-series data per minute and run SQL queries on petabytes of time-series data in seconds.

With InfluxDB support in Timestream, you can use a managed instance that is automatically configured for optimal performance and availability. Furthermore, you can increase resiliency by configuring multi-Availability Zone support for your InfluxDB databases.

Timestream for InfluxDB and Timestream for LiveAnalytics complement each other for low-latency and large-scale ingestion of time-series data.

Getting started with Timestream for InfluxDB
Let me show you how to get started.

First, I create an InfluxDB instance. I navigate to the Timestream console, go to InfluxDB databases in Timestream for InfluxDB and select Create Influx database.

On the next page, I specify the database credentials for the InfluxDB instance.

I also specify my instance class in Instance configuration and the storage type and volume to suit my needs.

In the next part, I can choose a multi-AZ deployment, which synchronously replicates data to a standby database in a different Availability Zone or just a single instance of InfluxDB. In the multi-AZ deployment, if a failure is detected, Timestream for InfluxDB will automatically fail over to the standby instance without data loss.

Then, I configure how to connect to my InfluxDB instance in Connectivity configuration. Here, I have the flexibility to define network type, virtual private cloud (VPC), subnets, and database port. I also have the flexibility to configure my InfluxDB instance to be publicly accessible by specifying public subnets and set the public access to Publicly Accessible, allowing Amazon Timestream will assign a public IP address to my InfluxDB instance. If you choose this option, make sure that you have proper security measures to protect your InfluxDB instances.

In this demo, I set my InfluxDB instance as Not publicly accessible, which also means I can only access it through the VPC and subnets I defined in this section.

Once I configure my database connectivity, I can define the database parameter group and the log delivery settings. In Parameter group, I can define specific configurable parameters that I want to use for my InfluxDB database. In the log delivery settings, I also can define which Amazon Simple Storage Service (Amazon S3) bucket I have to export the system logs. To learn more about the required AWS Identity and Access Management (IAM) policy for the Amazon S3 bucket, visit this page.

Once I’m happy with the configuration, I select Create Influx database.

Once my InfluxDB instance is created, I can see more information on the detail page.

With the InfluxDB instance created, I can also access the InfluxDB user interface (UI). If I configure my InfluxDB as publicly accessible, I can access the UI using the console by selecting InfluxDB UI. As shown on the setup, I configured my InfluxDB instance as not publicly accessible. In this case, I need to access the InfluxDB UI with SSH tunneling through an Amazon Elastic Compute Cloud (Amazon EC2) instance within the same VPC as my InfluxDB instance.

With the URL endpoint from the detail page, I navigate to the InfluxDB UI and use the username and password I configured in the creation process.

With access to the InfluxDB UI, I can now create a token to interact with my InfluxDB instance.

I can also use the Influx command line interface (CLI) to create a token. Before I can create the token, I create a configuration to interact with my InfluxDB instance. The following is the sample command to create a configuration:

influx config create --config-name demo  
    --host-url https://<TIMESTREAM for INFLUX DB ENDPOINT> 
   --org demo-org  
   --username-password [USERNAME] 
   --active

With the InfluxDB configuration created, I can now create an operator, all-access or read/write token. The following is an example for creating an all-access token to grant permissions to all resources in the organization that I defined:

influx auth create --org demo-org --all-access

With the required token for my use case, I can use various tools, such as the Influx CLI, Telegraf agent, and InfluxDB client libraries, to start ingesting data into my InfluxDB instance. Here, I’m using the Influx CLI to write sample home sensor data in the line protocol format, which you can also get from the InfluxDB documentation page.

influx write 
  --bucket demo-bucket 
  --precision s "
home,room=Living Room temp=21.1,hum=35.9,co=0i 1641024000
home,room=Kitchen temp=21.0,hum=35.9,co=0i 1641024000
home,room=Living Room temp=21.4,hum=35.9,co=0i 1641027600
home,room=Kitchen temp=23.0,hum=36.2,co=0i 1641027600
home,room=Living Room temp=21.8,hum=36.0,co=0i 1641031200
home,room=Kitchen temp=22.7,hum=36.1,co=0i 1641031200
home,room=Living Room temp=22.2,hum=36.0,co=0i 1641034800
home,room=Kitchen temp=22.4,hum=36.0,co=0i 1641034800
home,room=Living Room temp=22.2,hum=35.9,co=0i 1641038400
home,room=Kitchen temp=22.5,hum=36.0,co=0i 1641038400
home,room=Living Room temp=22.4,hum=36.0,co=0i 1641042000
home,room=Kitchen temp=22.8,hum=36.5,co=1i 1641042000
home,room=Living Room temp=22.3,hum=36.1,co=0i 1641045600
home,room=Kitchen temp=22.8,hum=36.3,co=1i 1641045600
home,room=Living Room temp=22.3,hum=36.1,co=1i 1641049200
home,room=Kitchen temp=22.7,hum=36.2,co=3i 1641049200
home,room=Living Room temp=22.4,hum=36.0,co=4i 1641052800
home,room=Kitchen temp=22.4,hum=36.0,co=7i 1641052800
home,room=Living Room temp=22.6,hum=35.9,co=5i 1641056400
home,room=Kitchen temp=22.7,hum=36.0,co=9i 1641056400
home,room=Living Room temp=22.8,hum=36.2,co=9i 1641060000
home,room=Kitchen temp=23.3,hum=36.9,co=18i 1641060000
home,room=Living Room temp=22.5,hum=36.3,co=14i 1641063600
home,room=Kitchen temp=23.1,hum=36.6,co=22i 1641063600
home,room=Living Room temp=22.2,hum=36.4,co=17i 1641067200
home,room=Kitchen temp=22.7,hum=36.5,co=26i 1641067200
"

Finally, I can query the data using the InfluxDB UI. I navigate to the Data Explorer page in the InfluxDB UI, create a simple Flux script, and select Submit.

Timestream for InfluxDB makes it easier for you to develop applications using InfluxDB, while continuing to use your existing tools to interact with the database. With the multi-AZ configuration, you can increase the availability of your InfluxDB data without worrying about the underlying infrastructure.

AWS and InfluxDB partnership
Celebrating this launch, here’s what Paul Dix, Founder and Chief Technology Officer at InfluxData, said about this partnership:

“The future of open source is powered by the public cloud—reaching the broadest community through simple entry points and practical user experience. Amazon Timestream for InfluxDB delivers on that vision. Our partnership with AWS turns InfluxDB open source into a force multiplier for real-time insights on time-series data, making it easier than ever for developers to build and scale their time-series workloads on AWS.”

Things to know
Here are some additional information that you need to know:

Availability – Timestream for InfluxDB is now generally available in the following AWS Regions: US East (Ohio, N. Virginia), US West (Oregon), Asia Pacific (Mumbai, Singapore, Sydney, Tokyo), and Europe (Frankfurt, Ireland, Stockholm).

Migration scenario – To migrate from a self-managed InfluxDB instance, you can simply restore a backup from an existing InfluxDB database into Timestream for InfluxDB. If you need to migrate from existing Timestream LiveAnalytics engine to Timestream for InfluxDB, you can leverage Amazon S3. Read more on how to do migration for various use cases on Migrating data from self-managed InfluxDB to Timestream for InfluxDB page.

Supported version – Timestream for InfluxDB currently supports the open source 2.7.5 version of InfluxDB

Pricing – To learn more about pricing, please visit Amazon Timestream pricing.

Demo – To see Timestream for InfluxDB in action, have a look at this demo created by my colleague, Derek:

Start building time-series applications and dashboards with millisecond response times using Timestream for InfluxDB. To learn more, visit Amazon Timestream for InfluxDB page.

Happy building!
Donnie

AWS Pi Day 2024: Use your data to power generative AI

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Today is AWS Pi Day! Join us live on Twitch, starting at 1 PM Pacific time.

On this day 18 years ago, a West Coast retail company launched an object storage service, introducing the world to Amazon Simple Storage Service (Amazon S3). We had no idea it would change the way businesses across the globe manage their data. Fast forward to 2024, every modern business is a data business. We’ve spent countless hours discussing how data can help you drive your digital transformation and how generative artificial intelligence (AI) can open up new, unexpected, and beneficial doors for your business. Our conversations have matured to include discussion around the role of your own data in creating differentiated generative AI applications.

Because Amazon S3 stores more than 350 trillion objects and exabytes of data for virtually any use case and averages over 100 million requests per second, it may be the starting point of your generative AI journey. But no matter how much data you have or where you have it stored, what counts the most is its quality. Higher quality data improves the accuracy and reliability of model response. In a recent survey of chief data officers (CDOs), almost half (46 percent) of CDOs view data quality as one of their top challenges to implementing generative AI.

This year, with AWS Pi Day, we’ll spend Amazon S3’s birthday looking at how AWS Storage, from data lakes to high performance storage, has transformed data strategy to becom the starting point for your generative AI projects.

This live online event starts at 1 PM PT today (March 14, 2024), right after the conclusion of AWS Innovate: Generative AI + Data edition. It will be live on the AWS OnAir channel on Twitch and will feature 4 hours of fresh educational content from AWS experts. Not only will you learn how to use your data and existing data architecture to build and audit your customized generative AI applications, but you’ll also learn about the latest AWS storage innovations. As usual, the show will be packed with hands-on demos, letting you see how you can get started using these technologies right away.

AWS Pi Day 2024

Data for generative AI
Data is growing at an incredible rate, powered by consumer activity, business analytics, IoT sensors, call center records, geospatial data, media content, and other drivers. That data growth is driving a flywheel for generative AI. Foundation models (FMs) are trained on massive datasets, often from sources like Common Crawl, which is an open repository of data that contains petabytes of web page data from the internet. Organizations use smaller private datasets for additional customization of FM responses. These customized models will, in turn, drive more generative AI applications, which create even more data for the data flywheel through customer interactions.

There are three data initiatives you can start today regardless of your industry, use case, or geography.

First, use your existing data to differentiate your AI systems. Most organizations sit on a lot of data. You can use this data to customize and personalize foundation models to suit them to your specific needs. Some personalization techniques require structured data, and some do not. Some others require labeled data or raw data. Amazon Bedrock and Amazon SageMaker offer you multiple solutions to fine-tune or pre-train a wide choice of existing foundation models. You can also choose to deploy Amazon Q, your business expert, for your customers or collaborators and point it to one or more of the 43 data sources it supports out of the box.

But you don’t want to create a new data infrastructure to help you grow your AI usage. Generative AI consumes your organization’s data just like existing applications.

Second, you want to make your existing data architecture and data pipelines work with generative AI and continue to follow your existing rules for data access, compliance, and governance. Our customers have deployed more than 1,000,000 data lakes on AWS. Your data lakes, Amazon S3, and your existing databases are great starting points for building your generative AI applications. To help support Retrieval-Augmented Generation (RAG), we added support for vector storage and retrieval in multiple database systems. Amazon OpenSearch Service might be a logical starting point. But you can also use pgvector with Amazon Aurora for PostgreSQL and Amazon Relational Database Service (Amazon RDS) for PostgreSQL. We also recently announced vector storage and retrieval for Amazon MemoryDB for Redis, Amazon Neptune, and Amazon DocumentDB (with MongoDB compatibility).

You can also reuse or extend data pipelines that are already in place today. Many of you use AWS streaming technologies such as Amazon Managed Streaming for Apache Kafka (Amazon MSK), Amazon Managed Service for Apache Flink, and Amazon Kinesis to do real-time data preparation in traditional machine learning (ML) and AI. You can extend these workflows to capture changes to your data and make them available to large language models (LLMs) in near real-time by updating the vector databases, make these changes available in the knowledge base with MSK’s native streaming ingestion to Amazon OpenSearch Service, or update your fine-tuning datasets with integrated data streaming in Amazon S3 through Amazon Kinesis Data Firehose.

When talking about LLM training, speed matters. Your data pipeline must be able to feed data to the many nodes in your training cluster. To meet their performance requirements, our customers who have their data lake on Amazon S3 either use an object storage class like Amazon S3 Express One Zone, or a file storage service like Amazon FSx for Lustre. FSx for Lustre provides deep integration and enables you to accelerate object data processing through a familiar, high performance file interface.

The good news is that if your data infrastructure is built using AWS services, you are already most of the way towards extending your data for generative AI.

Third, you must become your own best auditor. Every data organization needs to prepare for the regulations, compliance, and content moderation that will come for generative AI. You should know what datasets are used in training and customization, as well as how the model made decisions. In a rapidly moving space like generative AI, you need to anticipate the future. You should do it now and do it in a way that is fully automated while you scale your AI system.

Your data architecture uses different AWS services for auditing, such as AWS CloudTrail, Amazon DataZone, Amazon CloudWatch, and OpenSearch to govern and monitor data usage. This can be easily extended to your AI systems. If you are using AWS managed services for generative AI, you have the capabilities for data transparency built in. We launched our generative AI capabilities with CloudTrail support because we know how critical it is for enterprise customers to have an audit trail for their AI systems. Any time you create a data source in Amazon Q, it’s logged in CloudTrail. You can also use a CloudTrail event to list the API calls made by Amazon CodeWhisperer. Amazon Bedrock has over 80 CloudTrail events that you can use to audit how you use foundation models.

During the last AWS re:Invent conference, we also introduced Guardrails for Amazon Bedrock. It allows you to specify topics to avoid, and Bedrock will only provide users with approved responses to questions that fall in those restricted categories

New capabilities just launched
Pi Day is also the occasion to celebrate innovation in AWS storage and data services. Here is a selection of the new capabilities that we’ve just announced:

The Amazon S3 Connector for PyTorch now supports saving PyTorch Lightning model checkpoints directly to Amazon S3. Model checkpointing typically requires pausing training jobs, so the time needed to save a checkpoint directly impacts end-to-end model training times. PyTorch Lightning is an open source framework that provides a high-level interface for training and checkpointing with PyTorch. Read the What’s New post for more details about this new integration.

Amazon S3 on Outposts authentication caching – By securely caching authentication and authorization data for Amazon S3 locally on the Outposts rack, this new capability removes round trips to the parent AWS Region for every request, eliminating the latency variability introduced by network round trips. You can learn more about Amazon S3 on Outposts authentication caching on the What’s New post and on this new post we published on the AWS Storage blog channel.

Mountpoint for Amazon S3 Container Storage Interface (CSI) driver is available for Bottlerocket – Bottlerocket is a free and open source Linux-based operating system meant for hosting containers. Built on Mountpoint for Amazon S3, the CSI driver presents an S3 bucket as a volume accessible by containers in Amazon Elastic Kubernetes Service (Amazon EKS) and self-managed Kubernetes clusters. It allows applications to access S3 objects through a file system interface, achieving high aggregate throughput without changing any application code. The What’s New post has more details about the CSI driver for Bottlerocket.

Amazon Elastic File System (Amazon EFS) increases per file system throughput by 2x – We have increased the elastic throughput limit up to 20 GB/s for read operations and 5 GB/s for writes. It means you can now use EFS for even more throughput-intensive workloads, such as machine learning, genomics, and data analytics applications. You can find more information about this increased throughput on EFS on the What’s New post.

There are also other important changes that we enabled earlier this month.

Amazon S3 Express One Zone storage class integrates with Amazon SageMaker – It allows you to accelerate SageMaker model training with faster load times for training data, checkpoints, and model outputs. You can find more information about this new integration on the What’s New post.

Amazon FSx for NetApp ONTAP increased the maximum throughput capacity per file system by 2x (from 36 GB/s to 72 GB/s), letting you use ONTAP’s data management features for an even broader set of performance-intensive workloads. You can find more information about Amazon FSx for NetApp ONTAP on the What’s New post.

What to expect during the live stream
We will address some of these new capabilities during the 4-hour live show today. My colleague Darko will host a number of AWS experts for hands-on demonstrations so you can discover how to put your data to work for your generative AI projects. Here is the schedule of the day. All times are expressed in Pacific Time (PT) time zone (GMT-8):

  • Extend your existing data architecture to generative AI (1 PM – 2 PM).
    If you run analytics on top of AWS data lakes, you’re most of your way there to your data strategy for generative AI.
  • Accelerate the data path to compute for generative AI (2 PM – 3 PM).
    Speed matters for compute data path for model training and inference. Check out the different ways we make it happen.
  • Customize with RAG and fine-tuning (3 PM – 4 PM).
    Discover the latest techniques to customize base foundation models.
  • Be your own best auditor for GenAI (4 PM – 5 PM).
    Use existing AWS services to help meet your compliance objectives.

Join us today on the AWS Pi Day live stream.

I hope I’ll meet you there!

— seb

Anthropic’s Claude 3 Haiku model is now available on Amazon Bedrock

This post was originally published on this site

Last week, Anthropic announced their Claude 3 foundation model family. The family includes three models: Claude 3 Haiku, the fastest and most compact model for near-instant responsiveness; Claude 3 Sonnet, the ideal balanced model between skills and speed; and Claude 3 Opus, the most intelligent offering for top-level performance on highly complex tasks. AWS also announced the general availability of Claude 3 Sonnet in Amazon Bedrock.

Today, we are announcing the availability of Claude 3 Haiku on Amazon Bedrock. The Claude 3 Haiku foundation model is the fastest and most compact model of the Claude 3 family, designed for near-instant responsiveness and seamless generative artificial intelligence (AI) experiences that mimic human interactions. For example, it can read a data-dense research paper on arXiv (~10k tokens) with charts and graphs in less than three seconds.

With Claude 3 Haiku’s availability on Amazon Bedrock, you can build near-instant responsive generative AI applications for enterprises that need quick and accurate targeted performance. Like Sonnet and Opus, Haiku has image-to-text vision capabilities, can understand multiple languages besides English, and boasts increased steerability in a 200k context window.

Claude 3 Haiku use cases
Claude 3 Haiku is smarter, faster, and more affordable than other models in its intelligence category. It answers simple queries and requests with unmatched speed. With its fast speed and increased steerability, you can create AI experiences that seamlessly imitate human interactions.

Here are some use cases for using Claude 3 Haiku:

  • Customer interactions: quick and accurate support in live interactions, translations
  • Content moderation: catch risky behavior or customer requests
  • Cost-saving tasks: optimized logistics, inventory management, fast knowledge extraction from unstructured data

To learn more about Claude 3 Haiku’s features and capabilities, visit Anthropic’s Claude on Amazon Bedrock and Anthropic Claude models in the AWS documentation.

Claude 3 Haiku in action
If you are new to using Anthropic models, go to the Amazon Bedrock console and choose Model access on the bottom left pane. Request access separately for Claude 3 Haiku.

To test Claude 3 Haiku in the console, choose Text or Chat under Playgrounds in the left menu pane. Then choose Select model and select Anthropic as the category and Claude 3 Haiku as the model.

To test more Claude prompt examples, choose Load examples. You can view and run examples specific to Claude 3 Haiku, such as advanced Q&A with citations, crafting a design brief, and non-English content generation.

Using Compare mode, you can also compare the speed and intelligence between Claude 3 Haiku and the Claude 2.1 model using a sample prompt to generate personalized email responses to address customer questions.

By choosing View API request, you can also access the model using code examples in the AWS Command Line Interface (AWS CLI) and AWS SDKs. Here is a sample of the AWS CLI command:

aws bedrock-runtime invoke-model 
     --model-id anthropic.claude-3-haiku-20240307-v1:0 
     --body "{"messages":[{"role":"user","content":[{"type":"text","text":"Write the test case for uploading the image to Amazon S3 bucket\nCertainly! Here's an example of a test case for uploading an image to an Amazon S3 bucket using a testing framework like JUnit or TestNG for Java:\n\n...."}]}],"anthropic_version":"bedrock-2023-05-31","max_tokens":2000}" 
     --cli-binary-format raw-in-base64-out 
     --region us-east-1 
     invoke-model-output.txt

To make an API request with Claude 3, use the new Anthropic Claude Messages API format, which allows for more complex interactions such as image processing. If you use Anthropic Claude Text Completions API, you should upgrade from the Text Completions API.

Here is sample Python code to send a Message API request describing the image file:

def call_claude_haiku(base64_string):

    prompt_config = {
        "anthropic_version": "bedrock-2023-05-31",
        "max_tokens": 4096,
        "messages": [
            {
                "role": "user",
                "content": [
                    {
                        "type": "image",
                        "source": {
                            "type": "base64",
                            "media_type": "image/png",
                            "data": base64_string,
                        },
                    },
                    {"type": "text", "text": "Provide a caption for this image"},
                ],
            }
        ],
    }

    body = json.dumps(prompt_config)

    modelId = "anthropic.claude-3-haiku-20240307-v1:0"
    accept = "application/json"
    contentType = "application/json"

    response = bedrock_runtime.invoke_model(
        body=body, modelId=modelId, accept=accept, contentType=contentType
    )
    response_body = json.loads(response.get("body").read())

    results = response_body.get("content")[0].get("text")
    return results

To learn more sample codes with Claude 3, see Get Started with Claude 3 on Amazon Bedrock, Diagrams to CDK/Terraform using Claude 3 on Amazon Bedrock, and Cricket Match Winner Prediction with Amazon Bedrock’s Anthropic Claude 3 Sonnet in the Community.aws.

Now available
Claude 3 Haiku is available now in the US West (Oregon) Region with more Regions coming soon; check the full Region list for future updates.

Claude 3 Haiku is the most cost-effective choice. For example, Claude 3 Haiku is cheaper, up to 68 percent of the price per 1,000 input/output tokens compared to Claude Instant, with higher levels of intelligence. To learn more, see Amazon Bedrock Pricing.

Give Claude 3 Haiku a try in the Amazon Bedrock console today and send feedback to AWS re:Post for Amazon Bedrock or through your usual AWS Support contacts.

Channy

AWS Weekly Roundup — Claude 3 Sonnet support in Bedrock, new instances, and more — March 11, 2024

This post was originally published on this site

Last Friday was International Women’s Day (IWD), and I want to take a moment to appreciate the amazing ladies in the cloud computing space that are breaking the glass ceiling by reaching technical leadership positions and inspiring others to go and build, as our CTO Werner Vogels says.Now go build

Last week’s launches
Here are some launches that got my attention during the previous week.

Amazon Bedrock – Now supports Anthropic’s Claude 3 Sonnet foundational model. Claude 3 Sonnet is two times faster and has the same level of intelligence as Anthropic’s highest-performing models, Claude 2 and Claude 2.1. My favorite characteristic is that Sonnet is better at producing JSON outputs, making it simpler for developers to build applications. It also offers vision capabilities. You can learn more about this foundation model (FM) in the post that Channy wrote early last week.

AWS re:Post – Launched last week! AWS re:Post Live is a weekly Twitch livestream show that provides a way for the community to reach out to experts, ask questions, and improve their skills. The show livestreams every Monday at 11 AM PT.

Amazon CloudWatchNow streams daily metrics on CloudWatch metric streams. You can use metric streams to send a stream of near real-time metrics to a destination of your choice.

Amazon Elastic Compute Cloud (Amazon EC2)Announced the general availability of new metal instances, C7gd, M7gd, and R7gd. These instances have up to 3.8 TB of local NVMe-based SSD block-level storage and are built on top of the AWS Nitro System.

AWS WAFNow supports configurable evaluation time windows for request aggregation with rate-based rules. Previously, AWS WAF was fixed to a 5-minute window when aggregating and evaluating the rules. Now you can select windows of 1, 2, 5 or 10 minutes, depending on your application use case.

AWS Partners – Last week, we announced the AWS Generative AI Competency Partners. This new specialization features AWS Partners that have shown technical proficiency and a track record of successful projects with generative artificial intelligence (AI) powered by AWS.

For a full list of AWS announcements, be sure to keep an eye on the What’s New at AWS page.

Other AWS news
Some other updates and news that you may have missed:

One of the articles that caught my attention recently compares different design approaches for building serverless microservices. This article, written by Luca Mezzalira and Matt Diamond, compares the three most common designs for serverless workloads and explains the benefits and challenges of using one over the other.

And if you are interested in the serverless space, you shouldn’t miss the Serverless Office Hours, which airs live every Tuesday at 10 AM PT. Join the AWS Serverless Developer Advocates for a weekly chat on the latest from the serverless space.

Serverless office hours

The Official AWS Podcast – Listen each week for updates on the latest AWS news and deep dives into exciting use cases. There are also official AWS podcasts in several languages. Check out the ones in FrenchGermanItalian, and Spanish.

AWS Open Source News and Updates – This is a newsletter curated by my colleague Ricardo to bring you the latest open source projects, posts, events, and more.

Upcoming AWS events
Check your calendars and sign up for these AWS events:

AWS Summit season is about to start. The first ones are Paris (April 3), Amsterdam (April 9), and London (April 24). AWS Summits are free events that you can attend in person and learn about the latest in AWS technology.

GOTO x AWS EDA Day London 2024 – On May 14, AWS partners with GOTO bring to you the event-driven architecture (EDA) day conference. At this conference, you will get to meet experts in the EDA space and listen to very interesting talks from customers, experts, and AWS.

GOTO EDA Day 2022

You can browse all upcoming in-person and virtual events here.

That’s all for this week. Check back next Monday for another Week in Review!

— Marcia

This post is part of our Weekly Roundup series. Check back each week for a quick roundup of interesting news and announcements from AWS!

Amazon RDS now supports io2 Block Express volumes for mission-critical database workloads

This post was originally published on this site

Today, I am pleased to announce the availability of Provisioned IOPS (PIOPS) io2 Block Express storage volumes for all database engines in Amazon Relational Database Service (Amazon RDS). Amazon RDS provides you the flexibility to choose between different storage types depending on the performance requirements of your database workload. io2 Block Express volumes are designed for critical database workloads that require high performance and high throughput at low latency.

Lower latency and higher availability for I/O intensive workloads
With io2 Block Express volumes, your database workloads will benefit from consistent sub-millisecond latency, enhanced durability to 99.999 percent over io1 volumes, and drive 20x more IOPS from provisioned storage (up to 1,000 IOPS per GB) at the same price as io1. You can upgrade from io1 volumes to io2 Block Express volumes without any downtime, significantly improving the performance and reliability of your applications without increasing storage cost.

“We migrated all of our primary Amazon RDS instances to io2 Block Express within 2 weeks,” said Samir Goel, Director of Engineering at Figma, a leading platform for teams that design and build digital products. “Io2 Block Express has had a profound impact on the availability of the database layer at Figma. We have deeply appreciated the consistency of performance with io2 Block Express — in our observations, the latency variability has been under 0.1ms.”

io2 Block Express volumes support up to 64 TiB of storage, up to 256,000 Provisioned IOPS, and a maximum throughput of 4,000 MiB/s. The throughput of io2 Block Express volumes varies based on the amount of provisioned IOPS and volume storage size. Here is the range for each database engine and storage size:

Database engine Storage size Provisioned IOPS Maximum throughput
Db2, MariaDB, MySQL, and PostgreSQL Between 100 and 65,536 GiB 1,000–256,000 IOPS 4,000 MiB/s
Oracle Between 100 and 199 GiB 1,000–199,000 IOPS 4,000 MiB/s
Oracle Between 200 and 65,536 GiB 1,000–256,000 IOPS 4,000 MiB/s
SQL Server Between 20 and 16,384 GiB 1,000–64,000 IOPS 4,000 MiB/s

Getting started with io2 Block Express in Amazon RDS
You can use the Amazon RDS console to create a new RDS instance configured with an io2 Block Express volume or modify an existing instance with io1, gp2, or gp3 volumes.

Here’s how you would create an Amazon RDS for PostgreSQL instance with io2 Block Express volume.

Start with the basic information such as engine and version. Then, choose Provisioned IOPS SDD (io2) from the Storage type options:

Use the following AWS CLI command to create a new RDS instance with io2 Block Express volume:

aws rds create-db-instance --storage-type io2 --db-instance-identifier new-db-instance --db-instance-class db.t4g.large --engine mysql --master-username masteruser --master-user-password <enter password> --allocated-storage 400 --iops 3000

Similarly, to modify an existing RDS instance to use io2 Block Express volume:

aws rds modify-db-instance --db-instance-identifier existing-db-instance --storage-type io2 --allocated-storage 500 --iops 3000 --apply-immediately

Things to know

  • io2 Block Express volumes are available on all RDS databases using AWS Nitro System instances.
  • io2 Block Express volumes support an IOPS to allocated storage ratio of 1000:1. As an example, With an RDS for PostgreSQL instance, the maximum IOPS can be provisioned with volumes 256 GiB and larger (1,000 IOPS × 256 GiB = 256,000 IOPS).
  • For DB instances not based on the AWS Nitro System, the ratio of IOPS to allocated storage is 500:1. In this case, maximum IOPS can be achieved with 512 GiB volume (500 IOPS x 512 GiB = 256,000 IOPS).

Available now
Amazon RDS io2 Block Express storage volumes are supported for all RDS database engines and are available in US East (Ohio, N. Virginia), US West (N. California, Oregon), Asia Pacific (Hong Kong, Mumbai, Osaka, Seoul, Singapore, Sydney, Tokyo), Canada (Central), Europe (Frankfurt, Ireland, London, Stockholm), and Middle East (Bahrain) Regions.

In terms of pricing and billing, io1 volumes and io2 Block Express storage volumes are billed at the same rate. For more information, see the Amazon RDS pricing page.

Learn more by reading about Provisioned IOPS SSD storage in the Amazon RDS User Guide.

Abhishek

Free data transfer out to internet when moving out of AWS

This post was originally published on this site

You told us one of the primary reasons to adopt Amazon Web Services (AWS) is the broad choice of services we offer, enabling you to innovate, build, deploy, and monitor your workloads. AWS has continuously expanded its services to support virtually any cloud workload. It now offers over 200 fully featured services for compute, storage, databases, networking, analytics, machine learning (ML) and artificial intelligence (AI), and many more. For example, Amazon Elastic Compute Cloud (Amazon EC2) offers over 750 generally available instances—more than any other major cloud provider—and you can choose from numerous relational, analytics, key-value, document, or graph databases.

We believe this choice must include the one to migrate your data to another cloud provider or on-premises. That’s why, starting today, we’re waiving data transfer out to the internet (DTO) charges when you want to move outside of AWS.

Over 90 percent of our customers already incur no data transfer expenses out of AWS because we provide 100 gigabytes per month free from AWS Regions to the internet. This includes traffic from Amazon EC2, Amazon Simple Storage Service (Amazon S3), Application Load Balancer, among others. In addition, we offer one terabyte of free data transfer out of Amazon CloudFront every month.

If you need more than 100 gigabytes of data transfer out per month while transitioning, you can contact AWS Support to ask for free DTO rates for the additional data. It’s necessary to go through support because you make hundreds of millions of data transfers each day, and we generally do not know if the data transferred out to the internet is a normal part of your business or a one-time transfer as part of a switch to another cloud provider or on premises.

We will review requests at the AWS account level. Once approved, we will provide credits for the data being migrated. We don’t require you to close your account or change your relationship with AWS in any way. You’re welcome to come back at any time. We will, of course, apply additional scrutiny if the same AWS account applies multiple times for free DTO.

We believe in customer choice, including the choice to move your data out of AWS. The waiver on data transfer out to the internet charges also follows the direction set by the European Data Act and is available to all AWS customers around the world and from any AWS Region.

Freedom of choice is not limited to data transfer rates. AWS also supports Fair Software Licensing Principles, which make it easy to use software with other IT providers of your choice. You can read this blog post for more details.

You can check the FAQ for more information, or you can contact AWS Customer Support to request credits for DTO while switching.

But I sincerely hope you will not.

— seb

Anthropic’s Claude 3 Sonnet foundation model is now available in Amazon Bedrock

This post was originally published on this site

In September 2023, we announced a strategic collaboration with Anthropic that brought together their respective technology and expertise in safer generative artificial intelligence (AI), to accelerate the development of Anthropic’s Claude foundation models (FMs) and make them widely accessible to AWS customers. You can get early access to unique features of Anthropic’s Claude model in Amazon Bedrock to reimagine user experiences, reinvent your businesses, and accelerate your generative AI journeys.

In November 2023, Amazon Bedrock provided access to Anthropic’s Claude 2.1, which delivers key capabilities to build generative AI for enterprises. Claude 2.1 includes a 200,000 token context window, reduced rates of hallucination, improved accuracy over long documents, system prompts, and a beta tool use feature for function calling and workflow orchestration.

Today, Anthropic announced Claude 3, a new family of state-of-the-art AI models that allows customers to choose the exact combination of intelligence, speed, and cost that suits their business needs. The three models in the family are Claude 3 Haiku, the fastest and most compact model for near-instant responsiveness, Claude 3 Sonnet, the ideal balanced model between skills and speed, and Claude 3 Opus, a most intelligent offering for the top-level performance on highly complex tasks.

We’re also announcing the availability of Anthropic’s Claude 3 Sonnet today in Amazon Bedrock, with Claude 3 Opus and Claude 3 Haiku coming soon. For the vast majority of workloads, Claude 3 Sonnet model is two times faster than Claude 2 and Claude 2.1, with increased steerability, and new image-to-text vision capabilities.

With Claude 3 Sonnet’s availability in Amazon Bedrock, you can build cost-effective generative AI applications for enterprises that need intelligence, reliability, and speed. You can now use Anthropic’s latest model, Claude 3 Sonnet, in the Amazon Bedrock console.

Introduction of Anthropic’s Claude 3 Sonnet
Here are some key highlights about the new Claude 3 Sonnet model in Amazon Bedrock:

2x faster speed – Claude 3 has made significant gains in speed. For the vast majority of workloads, it is two times faster with the same level of intelligence as Anthropic’s most performant models, Claude 2 and Claude 2.1. This combination of speed and skill makes Claude 3 Sonnet the clear choice for tasks that require intelligent tasks demanding rapid responses, like knowledge retrieval or sales automation. This includes use cases like content generation, classification, data extraction, and research and retrieval or accurate searching over knowledge bases.

Increased steerability – Increased steerability of AI systems gives users more control over outputs and delivers predictable, higher-quality outcomes. It is significantly less likely to refuse to answer questions that border on the system’s guardrails to prevent harmful outputs. Claude 3 Sonnet is easier to steer and better at following directions in popular structured output formats like JSON—making it simpler for developers to build enterprise and frontier applications. This is particularly important in enterprise use cases such as autonomous vehicles, health and medical diagnoses, and algorithmic decision-making in sensitive domains such as financial services.

Image-to-text vision capabilities – Claude 3 offers vision capabilities that can process images and return text outputs. It is extremely capable at analyzing and understanding charts, graphs, technical diagrams, reports, and other visual assets. Claude 3 Sonnet achieves comparable performance to other best-in-class models with image processing capabilities, while maintaining a significant speed advantage.

Expanded language support – Claude 3 has improved understanding and responding in languages other than English, such as French, Japanese, and Spanish. This expanded language coverage allows Claude 3 Sonnet to better serve multinational corporations requiring AI services across different geographies and languages, as well as businesses requiring nuanced translation services. Claude 3 Sonnet is also stronger at coding and mathematics, as evidenced by Anthropic’s scores in evaluations such as grade-school math problems (GSM8K and Hendrycks) and Codex (HumanEval).

To learn more about Claude 3 Sonnet’s features and capabilities, visit Anthropic’s Claude on Amazon Bedrock and Anthropic Claude model in the AWS documentation.

Get started with Anthropic’s Claude 3 Sonnet in Amazon Bedrock
If you are new to using Anthropic models, go to the Amazon Bedrock console and choose Model access on the bottom left pane. Request access separately for Claude 3 Sonnet.

To test Claude 3 Sonnet in the console, choose Text or Chat under Playgrounds in the left menu pane. Then choose Select model and select Anthropic as the category and Claude 3 Sonnet as the model.

To test more Claude prompt examples, choose Load examples. You can view and run Claude 3 specific examples, such as advanced Q&A with citations, crafting a design brief, and non-English content generation.

By choosing View API request, you can also access the model via code examples in the AWS Command Line Interface (AWS CLI) and AWS SDKs. Here is a sample of the AWS CLI command:

aws bedrock-runtime invoke-model 
--model-id anthropic.claude-3-sonnet-v1:0 
--body "{"prompt":"Write the test case for uploading the image to Amazon S3 bucketnHere are some test cases for uploading an image to an Amazon S3 bucket:nn1. **Successful Upload Test Case**:n   - Test Data:n     - Valid image file (e.g., .jpg, .png, .gif)n     - Correct S3 bucket namen     - Correct AWS credentials (access key and secret access key)n   - Steps:n     1. Initialize the AWS S3 client with the correct credentials.n     2. Open the image file.n     3. Upload the image file to the specified S3 bucket.n     4. Verify that the upload was successful.n   - Expected Result: The image should be successfully uploaded to the S3 bucket.nn2. **Invalid File Type Test Case**:n   - Test Data:n     - Invalid file type (e.g., .txt, .pdf, .docx)n     - Correct S3 bucket namen     - Correct AWS credentialsn   - Steps:n     1. Initialize the AWS S3 client with the correct credentials.n     2. Open the invalid file type.n     3. Attempt to upload the file to the specified S3 bucket.n     4. Verify that an appropriate error or exception is raised.n   - Expected Result: The upload should fail with an error or exception indicating an invalid file type.nnThese test cases cover various scenarios, including successful uploads, invalid file types, invalid bucket names, invalid AWS credentials, large file uploads, and concurrent uploads. By executing these test cases, you can ensure the reliability and robustness of your image upload functionality to Amazon S3.","max_tokens_to_sample":2000,"temperature":1,"top_k":250,"top_p":0.999,"stop_sequences":["nnHuman:"],"anthropic_version":"bedrock-2023-05-31"}" 
--cli-binary-format raw-in-base64-out 
--region us-east-1 
invoke-model-output.txt

Upload your image if you want to test image-to-text vision capabilities. I uploaded the featured image of this blog post and received a detailed description of this image.

You can process images via API and return text outputs in English and multiple other languages.

{
  "modelId": "anthropic.claude-3-sonnet-v1:0",
  "contentType": "application/json",
  "accept": "application/json",
  "body": {
    "anthropic_version": "bedrock-2023-05-31",
    "max_tokens": 1000,
    "system": "Please respond only in Spanish.",
    "messages": {
      "role": "user",
      "content": [
        {
          "type": "image",
          "source": {
            "type": "base64",
            "media_type": "image/jpeg",
            "data": "iVBORw..."
          }
        },
        {
          "type": "text",
          "text": "What's in this image?"
        }
      ]
    }
  }
}

To celebrate this launch, Neerav Kingsland, Head of Global Accounts at Anthropic, talks about the power of the Anthropic and AWS partnership.

“Anthropic at its core is a research company that is trying to create the safest large language models in the world, and through Amazon Bedrock we have a change to take that technology, distribute it to users globally, and do this in an extremely safe and data-secure manner.”

Now available
Claude 3 Sonnet is available today in the US East (N. Virginia) and US West (Oregon) Regions; check the full Region list for future updates. The availability of Anthropic’s Claude 3 Opus and Haiku in Amazon Bedrock also will be coming soon.

You will be charged for model inference and customization with the On-Demand and Batch mode, which allows you to use FMs on a pay-as-you-go basis without having to make any time-based term commitments. With the Provisioned Throughput mode, you can purchase model units for a specific base or custom model. To learn more, see Amazon Bedrock Pricing.

Give Anthropic’s Claude 3 Sonnet a try in the Amazon Bedrock console today and send feedback to AWS re:Post for Amazon Bedrock or through your usual AWS Support contacts.

Channy

AWS Weekly Roundup — .Net Runtime for AWS Lambda, PartyRock Hackathon, and more — February 26, 2024

This post was originally published on this site

The Community AWS re:invent 2023 re:caps continue! Recently, I was invited to participate in one of these events hosted by the AWS User Group Kenya, and was able to learn and spend time with this amazing community.

AWS User Group Kenya

AWS User Group Kenya

Last week’s launches
Here are some launches that got my attention during the previous week.

.NET 8 runtime for AWS Lambda – AWS Lambda now supports .NET 8 as both a managed runtime and container base image. This support provides you with .NET 8 features that include API enhancements, improved Native Ahead of Time (Native AOT) support, and improved performance. .NET 8 supports C# 12, F# 8, and PowerShell 7.4. You can develop Lambda functions in .NET 8 using the AWS Toolkit for Visual Studio, the AWS Extensions for .NET CLI, AWS Serverless Application Model (AWS SAM), AWS CDK, and other infrastructure as code tools.

For a full list of AWS announcements, be sure to keep an eye on the What’s New at AWS page.

Other AWS news
Here are some additional projects, programs, and news items that you might find interesting:

Earlier this month, I used this image to call attention to the PartyRock Hackathon that’s currently in progress. The deadline to join the hackathon is fast approaching so be sure to signup before time runs out.

Amazon API Gateway – Amazon API Gateway processed over 100 trillion API requests in 2023, and we continue to see growing demand for API-driven applications. API Gateway is a fully-managed service that enables you to create, publish, maintain, monitor, and secure APIs at any scale. Customers that onboarded large workloads on API Gateway in 2023 told us they chose the service for its availability, security, and serverless architecture. Those in regulated industries value API Gateway’s private endpoints, which are isolated from the public internet and only accessible from your Amazon Virtual Private Cloud (VPC).

AWS open source news and updates – My colleague Ricardo writes this weekly open source newsletter in which he highlights new open source projects, tools, and demos from the AWS Community.

Upcoming AWS events
Season 3 of the Build on Generative AI Twitch show has kicked off. Join every Monday on Twitch at 9AM PST/Noon EST/18h CET to learn among others, how you can build generative AI-enabled applications.

If you’re in the EMEA timezone, there is still time to register and watch the AWS Innovate Online Generative AI & Data Edition taking place on February 29. Innovate Online events are free, online, and designed to inspire and educate you about building on AWS. Whether you’re in the Americas, Asia Pacific & Japan, or EMEA region, learn here about future AWS Innovate Online events happening in your timezone.

AWS Community re:Invent re:Caps – Join a Community re:Cap event organized by volunteers from AWS User Groups and AWS Cloud Clubs around the world to learn about the latest announcements from AWS re:Invent.

You can browse all upcoming in-person and virtual events here.

That’s all for this week. Check back next Monday for another Weekly Roundup!

Veliswa

This post is part of our Weekly Roundup series. Check back each week for a quick roundup of interesting news and announcements from AWS.

New AWS Region in Mexico is in the works

This post was originally published on this site

Today, I am happy to announce that we are working on an AWS Region in Mexico. This AWS Mexico (Central) Region will be the second Region in Latin America joining the AWS South America (São Paulo) Region and will give AWS customers the ability to run workloads and store data that must remain in-country.

Mexico in the works

The Region will include three Availability Zones, each one physically independent of the others in the Region yet far enough apart to minimize the risk that an event in one Availability Zone will have impact on business continuity. The Availability Zones will be connected to each other by high-bandwidth, low-latency network connections over dedicated, fully redundant fiber.

With this announcement, AWS now has five new Regions in the works (Germany, Malaysia, Mexico, New Zealand, and Thailand) and 15 upcoming new Availability Zones.

AWS investment in Mexico

The upcoming AWS Mexico Region is the latest in ongoing investments by AWS in Mexico to provide customers with advanced and secure cloud technologies. Since 2020, AWS has launched seven Amazon CloudFront edge locations in Mexico. Amazon CloudFront is a highly secure and programmable content delivery network (CDN) that accelerates the delivery of data, videos, applications, and APIs to users worldwide with low latency and high transfer speeds.

In 2020, AWS launched AWS Outposts in Mexico. AWS Outposts is a family of fully managed solutions delivering AWS infrastructure and services to virtually any on-premises or edge location for a truly consistent hybrid experience. AWS expanded its infrastructure footprint in Mexico again in 2023 with the launch of AWS Local Zones in Queretaro. AWS Local Zones are a type of AWS infrastructure deployment that places compute, storage, database, and other select services closer to large population, industry, and IT centers, enabling customers to deliver applications that require single-digit millisecond latency to end users. In 2023, AWS established an AWS Direct Connect location in Queretaro, allowing customers to establish private connectivity between AWS and their data center, office, or colocation environment.

Here is a glimpse into our customers in Mexico and the exciting, innovative work they’re undertaking:

Banco Santander Mexico is one of the leading financial groups in the country, focused on commercial banking and securities financing, serving more than 20.5 million customers. “AWS has been a strategic partner for our digital transformation,” said Juan Pablo Chiappari, head of IT Infrastructure for North America. “Thanks to their wide range of services, we have been able to innovate faster, improve our customer experience and reduce our operating costs.”

SkyAlert is an innovative technology company that quickly alerts millions of people living in earthquake-prone areas, promoting a culture of prevention against natural disasters. In order to provide customers—both businesses and individuals—with the right tools to protect themselves during earthquakes, SkyAlert migrated its infrastructure to AWS. After implementing its Internet of Things (IoT) solution to run on AWS and its efficient alert service, SkyAlert scales quickly and can send millions of messages in a few seconds, helping to save lives in the event of earthquakes.

Kueski is an online lender for the middle class of Mexico and Latin America. The company uses big data and advanced analytics to approve and deliver loans in a matter of minutes. The company has become the fastest-growing platform of its kind in the region and has already granted thousands of loans. They were born with AWS.

Bolsa Institucional de Valores (BIVA) is a stock exchange based in Mexico, backed by Nasdaq. BIVA provides local and global investors with cutting-edge technology for trading and market solutions and companies with listing and maintenance services. As part of its vision of innovation, BIVA started its journey to the cloud in 2023 by migrating its disaster recovery site, including its trading and market surveillance systems, to AWS, using edge compute capabilities available in both the AWS Local Zones in Queretaro, Mexico, to achieve their low latency needs.

Stay Tuned
The AWS Region in Mexico will open in early 2025. As usual, subscribe to this blog so that you will be among the first to know when the new Region is open!

To learn more about AWS Global Cloud Infrastructure, see the Global Infrastructure page.

— Irshad