Andy Jassy - CEO, Amazon Web Services
Amazon S3 Access Points
Amazon S3 Access Points is a new S3 feature that simplifies managing data access at scale for shared data sets on Amazon S3. With S3 Access Points, users can create hundreds of access points per bucket, each with a name and permissions customised for the application.
According to AWS, this capability represents a new way of provisioning access to shared data sets. Whether creating an access point for data ingestion, transformation, restricted read access, or unrestricted access, using S3 Access Points simplifies the work of creating and maintaining access to shared S3 buckets.
Moreover, S3 Access Points policies allow enforcing permissions by prefixes and object tags, allowing limits on the object data that can be accessed. Any S3 Access Points can be restricted to a Virtual Private Cloud (VPC) to firewall S3 data access within a user’s private networks.
Amazon Augmented AI
Amazon Augmented AI (Amazon A2I) makes it easy for users to build the workflows required for human review of ML predictions. A2I is designed to bring human review to all developers, removing the undifferentiated heavy lifting associated with building human review systems or managing large numbers of human reviewers.
Broadly, Amazon A2I provides built-in human review workflows for common machine learning use cases, such as content moderation and text extraction from documents, which allows predictions from Amazon Rekognition and Amazon Textract to be reviewed easily.
Users can also create their own workflows for ML models built on Amazon SageMaker or any other tools. Using A2I, users can allow human reviewers to step in when a model is unable to make a high confidence prediction or to audit its predictions on an on-going basis.
Accelerated Site-to-Site VPN for Improved VPN Performance
Amazon has announced the availability of Accelerated Site-to-Site VPN, which uses AWS Global Accelerator to improve the performance of VPN connections by intelligently routing traffic through the AWS Global Network and AWS edge locations.
“Previously, VPN connections might face inconsistent performance as traffic traverses multiple public networks to reach a VPN endpoint in AWS. Public networks, such as the public internet, can be congested. Each hop between and within public networks can introduce performance risks,” the company said.
Now, when creating an AWS Site-to-Site VPN connection to an AWS Transit Gateway, users can now enable Acceleration to take advantage of performance improvement using the AWS global network.
Amazon Redshift data lake export, in Apache Parquet format
With this new functionality, users can now unload the result of an Amazon Redshift query to their Amazon S3 data lake as Apache Parquet, an efficient open columnar storage format for analytics, according to Amazon.
“The Parquet format is up to 2x faster to unload and consumes up to 6x less storage in Amazon S3, compared to text formats,” the company said. “This enables you to save data transformation and enrichment you have done in Amazon Redshift into your Amazon S3 data lake in an open format.
“You can then analyze your data with Redshift Spectrum and other AWS services such as Amazon Athena, Amazon EMR, and Amazon SageMaker.”
AWS Transit Gateway now supports Inter-Region Peering
AWS Transit Gateway now supports the ability to establish peering connections between Transit Gateways in different AWS Regions. The service enables customers to connect thousands of Amazon Virtual Private Clouds (Amazon VPCs) and their on-premises networks using a single gateway.
“With AWS Transit Gateway, customers only have to create and manage a single connection from a central regional gateway to each Amazon VPC, on premises data center, or remote office across their networks,” the company said.
Inter-region Transit Gateway peering is available in US East (N. Virginia), US East (Ohio), US West (Oregon), EU (Ireland), and EU (Frankfurt) AWS Regions. Support for other AWS Regions is coming soon.
AWS DeepComposer
AWS has announced the preview of AWS DeepComposer, which it claims is the world’s first machine learning-enabled keyboard for developers.
“Get hands-on, literally, with a musical keyboard and the latest machine learning techniques to compose your own music,” the company said. “With AWS DeepComposer keyboard, you can create a melody that will transform into a completely original song in seconds, all powered by AI.
“AWS DeepComposer includes tutorials, sample code, and training data that can be used to get started building generative models, all without having to write a single line of code,” it said.
According to Amazon, generative AI is one of the biggest advancements in artificial intelligence technology and, until now, developers interested in growing skills in this area haven’t had an easy way to get started. Developers, regardless of their background in ML or music, can now get started with Generative Adversarial Networks (GANs).
“This Generative AI technique pits two different neural networks against each other to produce new and original digital works based on sample inputs. With AWS DeepComposer, you can train and optimise GAN models to create original music,” the company said.
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