Amazon Transcribe Now Supports Automatic Language Identification

In 2017, we launched Amazon Transcribe, an automatic speech recognition service that makes it easy for developers to add a speech-to-text capability to their applications. Since then, we added support for more languages, enabling customers globally to transcribe audio recordings in 31 languages, including 6 in real-time.
A popular use case for Amazon Transcribe is transcribing customer calls. This allows companies to analyze the transcribed text using natural language processing techniques to detect sentiment or to identify the most common call causes. If you operate in a country with multiple official languages or across multiple regions, your audio files can contain different languages. Thus, files have to be tagged manually with the appropriate language before transcription can take place. This typically involves setting up teams of multi-lingual speakers, which creates additional costs and delays in processing audio files.
The media and entertainment industry often uses Amazon Transcribe to convert media content


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Amazon ECS Now Supports EC2 Inf1 Instances

As machine learning and deep learning models become more sophisticated, hardware acceleration is increasingly required to deliver fast predictions at high throughput. Today, we’re very happy to announce that AWS customers can now use the Amazon EC2 Inf1 instances on Amazon ECS, for high performance and the lowest prediction cost in the cloud. For a few weeks now, these instances have also been available on Amazon Elastic Kubernetes Service.
A primer on EC2 Inf1 instancesInf1 instances were launched at AWS re:Invent 2019. They are powered by AWS Inferentia, a custom chip built from the ground up by AWS to accelerate machine learning inference workloads.
Inf1 instances are available in multiple sizes, with 1, 4, or 16 AWS Inferentia chips, with up to 100 Gbps network bandwidth and up to 19 Gbps EBS bandwidth. An AWS Inferentia chip contains four NeuronCores. Each one implements a high-performance systolic array matrix multiply engine,


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Find Your Most Expensive Lines of Code – Amazon CodeGuru Is Now Generally Available

Bringing new applications into production, maintaining their code base as they grow and evolve, and at the same time respond to operational issues, is a challenging task. For this reason, you can find many ideas on how to structure your teams, on which methodologies to apply, and how to safely automate your software delivery pipeline.
At re:Invent last year, we introduced in preview Amazon CodeGuru, a developer tool powered by machine learning that helps you improve your applications and troubleshoot issues with automated code reviews and performance recommendations based on runtime data. During the last few months, many improvements have been launched, including a more cost-effective pricing model, support for Bitbucket repositories, and the ability to start the profiling agent using a command line switch, so that you no longer need to modify the code of your application, or add dependencies, to run the agent.

You can use CodeGuru in two ways:
CodeGuru Reviewer uses


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Reinventing Enterprise Search – Amazon Kendra is Now Generally Available

At the end of 2019, we launched a preview version of Amazon Kendra, a highly accurate and easy to use enterprise search service powered by machine learning. Today, I’m very happy to announce that Amazon Kendra is now generally available.
For all its amazing achievements in past decades, Information Technology had yet to solve a problem that all of us face every day: quickly and easily finding the information we need. Whether we’re looking for the latest version of the company travel policy, or asking a more technical question like “what’s the tensile strength of epoxy adhesives?”, we never seem to be able to get the correct answer right away. Sometimes, we never get it at all!
Not only are these issues frustrating for users, they’re also responsible for major productivity losses. According to an IDC study, the cost of inefficient search is $5,700 per employee per year: for a


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New – Building a Continuous Integration Workflow with Step Functions and AWS CodeBuild

Automating your software build is an important step to adopt DevOps best practices. To help you with that, we built AWS CodeBuild, a fully managed continuous integration service that compiles source code, runs tests, and produces packages that are ready for deployment.
However, there are so many possible customizations in our customers’ build processes, and we have seen developers spend time in creating their own custom workflows to coordinate the different activities required by their software build. For example, you may want to run, or not, some tests, or skip static analysis of your code when you need to deploy a quick fix. Depending on the results of your unit tests, you may want to take different actions, or be notified via SNS.
To simplify that, we are launching today a new AWS Step Functions service integration with CodeBuild. Now, during the execution of a state machine, you can start or stop a build, get build report summaries,


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General Availability of UltraWarm for Amazon Elasticsearch Service

Today, we are happy to announce the general availability of UltraWarm for Amazon Elasticsearch Service.
This new low-cost storage tier provides fast, interactive analytics on up to three petabytes of log data at one-tenth of the cost of the current Amazon Elasticsearch Service storage tier.
UltraWarm, complements the existing Amazon Elasticsearch Service hot storage tier by providing less expensive storage for older and less-frequently accessed data while still ensuring that snappy, interactive experience that Amazon Elasticsearch Service customers have come to expect. Amazon Elasticsearch Service stores data in Amazon S3 while using custom, highly-optimized nodes, purpose-built on the AWS Nitro System, to cache, pre-fetch, and query that data.
There are many use cases for the Amazon Elasticsearch Service, from building a search system for your website, storing, and analyzing data from application or infrastructure logs. We think this new storage tier will work particularly well for customers that have large


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AWS Chatbot – ChatOps for Slack and Chime

Last year, my colleague Ilya Bezdelev wrote Introducing AWS Chatbot: ChatOps for AWS to launch the public beta of AWS Chatbot. He also participated in the re:Invent 2019 Launchpad and did an in-depth AWS Chatbot demo:

In his initial post, Ilya showed you how you can practice ChatOps within Amazon Chime or Slack, receiving AWS notifications and executing commands in an environment that is intrinsically collaborative. In a later post, Running AWS commands from Slack using AWS Chatbot, Ilya showed how to configure AWS Chatbot in a Slack channel, display CloudWatch alarms, describe AWS resources, invoke a Lambda function and retrieve the logs, and create an AWS Support case. My colleagues Erin Carlson and Matt Cowsert wrote about AWS Budgets Integration with Chatbot and walked through the process of setting up AWS Budget alerts and arranging for notifications from within AWS Chatbot. Finally, Anushri Anwekar showed how to Receive


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Announcing TorchServe, An Open Source Model Server for PyTorch

PyTorch is one of the most popular open source libraries for deep learning. Developers and researchers particularly enjoy the flexibility it gives them in building and training models. Yet, this is only half the story, and deploying and managing models in production is often the most difficult part of the machine learning process: building bespoke prediction APIs, scaling them, securing them, etc.
One way to simplify the model deployment process is to use a model server, i.e. an off-the-shelf web application specially designed to serve machine learning predictions in production. Model servers make it easy to load one or several models, automatically creating a prediction API backed by a scalable web server. They’re also able to run preprocessing and postprocessing code on prediction requests. Last but not least, model servers also provide production-critical features like logging, monitoring, and security. Popular model servers include TensorFlow Serving and the Multi Model Server.


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Amazon Elastic Container Service now supports Amazon EFS file systems

It has only been five years since Jeff wrote on this blog about the launch of the Amazon Elastic Container Service. I remember reading that post and thinking how exotic and unusual containers sounded. Fast forward just five years, and containers are an everyday part of most developers lives, but whilst customers are increasingly adopting container orchestrators such as ECS, there are still some types of applications that have been hard to move into this containerized world.
Customers building applications that require data persistence or shared storage have faced a challenge since containers are temporary in nature. As containers are scaled in and out dynamically, any local data is lost as containers are terminated. Today we are changing that for ECS by launching support for Amazon Elastic File System (EFS) file systems. Both containers running on ECS and AWS Fargate will be able to use Amazon Elastic File System (EFS).


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