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Key Announcements From Amazon re:Invent 2021

AWS announces new tools, software and upgrades for its existing products to boost AI Productivity, cybersecurity, and more at its annual re:Invent Event.

Every year at re:Invent, Amazon Web Services (AWS) showcases its latest products, innovations, and developer solutions. AWS opted to keep it modest last year, which resulted in a slew of announcements this year. AWS Private 5G, the new ARM-based Graviton 3 chips, new AWS data centers in the Netherlands and Belgium (local zones), AWS SageMaker machine learning solutions, a new low-code development tool, AWS Amplify Studio, and numerous AWS additions are just a few of the key announcements that dotted the event which was held in Las Vegas.

Your guide to Amazon Connect at re:Invent 2021 | AWS Contact Center
Image Source: AWS

We have listed some of the key announcements from AWS re:Invent 2021. It is important to note that this is not a ranked list.

1. Graviton3 and Trn1

AWS introduced a new Graviton chip, called Graviton3, which is the latest version of Amazon’s own ARM-based processor for AI inferencing applications. Graviton3 is up to 25% quicker for general-compute tasks, with two times faster floating-point performance for scientific workloads, two times faster performance for cryptography workloads, and three times faster performance for machine learning workloads, according to Selipsky. Furthermore, according to Selipsky, Graviton3 uses up to 60% less energy for the same performance as the previous version.

Another important key highlight of the re:Invent 2021 was the announcement of Amazon’s latest machine learning chip, the Trn1. The new Trainium-powered Trn1 instance can provide the greatest price-performance for deep learning model training in the cloud, as well as the quickest on EC2. Trn1 is the first EC2 instance with up to 800 megabytes per second bandwidth, according to Adam Selipsky, CEO of AWS, making it ideal for large-scale, multi-node distributed training use cases such as image recognition, natural language processing, fraud detection, and forecasting.

2. New SageMaker Features

Amazon SageMaker is a managed service for building, training and deploying machine learning (ML) models. During the re:Invent, AWS announced numerous additions to the existing SageMaker tools and features at re:Invent 2021. 

AWS has introduced the SageMaker Ground Truth Plus service, which employs an expert workforce to produce high-quality training datasets more quickly. SageMaker Ground Truth Plus employs a labeling workflow that includes active learning, pre-labeling, and machine validation approaches, as well as machine learning techniques. The new service, according to the company, saves up to 40% on expenditures and doesn’t need consumers to have extensive machine learning knowledge. Users can access this service to construct training datasets without having to write their own labeling apps. 

A new SageMaker Inference Recommender tool was also released to assist customers in selecting the best available compute instance for deploying machine learning models for optimal performance and cost. According to AWS, the tool chooses the appropriate compute instance type, instance count, container settings, and model optimizations automatically. Except for AWS China, Amazon SageMaker Inference Recommender is available in all locations where SageMaker is offered.

AWS also unveiled Amazon SageMaker Training Compiler, a new SageMaker technology that can speed up deep learning (DL) model training by up to 50% by making better use of GPU instances.

Deep learning model fine-tuning might take days, resulting in exorbitant expenses and a slowdown in innovation. You may now employ SageMaker Training Compiler to speed up this process by making minor modifications to your existing training script. SageMaker Training Compiler is built into the most recent versions of PyTorch and TensorFlow in SageMaker and operates behind the scenes of both frameworks, requiring no further modifications to your workflow once enabled.

3. No-code and Low code Solutions

Some of AWS’s big announcements at its re:Invent 2021 users conference were based on low-code and no-code. AWS touts the debut of Amazon SageMaker Canvas as a no-code for machine learning, claiming that it allows business analysts to develop ML models for predictions without knowing how to code or having ML experience. Business users may easily integrate files from the cloud and on-premises data sources to make forecasts for the delivery of products using its intuitive graphical user interface. The no-code Canvas may be thought of as a basic user interface for Amazon’s SageMaker AutoML features.

Meanwhile, Amplify Studio, another product unveiled at re:Invent 2021, is a low-code platform-as-a-service for web and mobile app development, is now made available in public preview on AWS. Amplify Studio is a visual development tool that allows developers to take a designer’s Figma file and instantly transform it into React UI component code, which can then be connected to back-end resources and allows changes to be made using a visual development interface. Amplify Studio is an augmentation of AWS’s earlier Amplify service, which focused on creating web and mobile apps but lacked Amplify Studio’s simple drag-and-drop interface.

4. Amazon Lex

AWS company announced the Amazon Lex automated chatbot designer in preview, a new product that automates the chatbot training and creation process.

Lex accomplishes this by utilizing advanced natural language interpretation capabilities aided by deep learning techniques. According to Swami Sivasubramanian, VP of Amazon AI, developers can now use historical call transcripts to build a fundamental chatbot in only a few clicks. Within a few hours, Amazon Lex automated chatbot designer can scan 10,000 lines of transcripts to recognize intents like ‘submit a new claim’ or ‘check claim status.’ It ensures that these intents are adequately segregated and that none of them overlap, removing the need for a trial-and-error method.

5. IoT RoboRunner

AWS IoT RoboRunner is a new robotics service from Amazon that makes it easier for businesses to create and deploy apps that allow fleets of robots to collaborate. IoT RoboRunner, which is now in beta, leverages robots management technology that is already in use at Amazon warehouses. It enables AWS customers to integrate robots and current automation software to synchronize work across operations, merging data from each kind of robot in a fleet and unifying data types such as facility, location, and robotic job data in a single repository. IoT RoboRunner may also be used to give measurements and KPIs to administrative dashboards through APIs.

6. AWS Robotics Startup Accelerator

Along with IoT RoboRunner, Amazon also introduced the AWS Robotics Startup Accelerator, a joint venture with nonprofit MassRobotics to address difficulties in automation, robotics, and industrial internet of things (IoT) technology, at re:Invent 2021.

The Robotics Startup Accelerator will help robotics startups create, prototype, test, and market their products and services. AWS and MassRobotics specialists will offer consultancy to startups selected into the Robotics Startup Program on business models, while AWS robotics engineers will provide technical support. Some of the additional advantages include Hands-on training on AWS robotics solutions, as well as up to $10,000 in promotional credits to utilize AWS IoT, robotics, and machine learning services. Startups can also benefit from MassRobotics’ business development and investment advice, as well as co-marketing possibilities with AWS through blogs and case studies.

7. Karpenter

Karpenter, a new open-source autoscaling solution for Kubernetes clusters, was unveiled by AWS. One of the benefits of cloud computing is its capacity to automatically grow to suit your resource requirements. Administrators managing Kubernetes clusters, on the other hand, have had to keep a close eye on them in order to ensure that they had the correct number of resources and avoid service outages.

Karpenter was created to make the cloud computing dream a reality. It improves application availability and cluster efficiency by deploying the relevant computing resources quickly in response to changing application demand. It also offers just-in-time computing resources to fulfill your application’s requirements, and will soon optimize a cluster’s compute resource footprint to increase performance. In case the nodes are no more needed, it automatically terminates them, thereby cutting the infrastructure cost. 

To calculate your Kubernetes workloads, Karpenter uses Helm, the Kubernetes package management. It also needs permission to automate the provisioning of computational resources.

Read More: Key Announcements at Microsoft Ignite Fall Edition 2021: Day 1

8. AWS Data Exchange for APIs

Companies can acquire or extract information that matches their needs by using an API. For example, to retrieve location information, you may use the Google Maps API. That’s fantastic for a single API, but if you’re employing many APIs, it might lead to a whole new set of issues with communication, authentication, and API governance.

To address this, AWS unveiled the AWS Data Exchange for APIs at re:Invent 2021, a new solution that automatically updates changing third-party APIs, eliminating the need to design the updating mechanism.

If a user is developing an application or data model on AWS, this tool allows them to use AWS SDKs and leverage AWS authentication and governance tooling to access and update third-party APIs in an automated manner. Third-party data providers can also publish their APIs in the Data Exchange catalog to make their data sources available to developers.

9. Textract

Textract, Amazon’s machine learning tool that extracts text, handwriting, and data from scanned documents, now supports identifying papers such as licenses and passports, according to the company. Users may automatically extract specific as well as inferred information from IDs, such as expiry date, date of birth, name, and address, without the need for templates or settings.

10. Container Security

AWS introduced pull-through cache repository support in Amazon Elastic Container Registry to assist development teams who use containers from publically available registries in securing the containers.

For container images sourced from public registries, the feature will provide developers with increased speed, security, and availability of the Amazon Elastic Container Registry.

Images in pull-through cache repositories, according to Amazon, are automatically kept in sync with upstream public registries, removing the need for human image downloading and updating. Furthermore, pull through cache repositories benefit from Amazon Elastic Container Registry’s built-in security features, such as AWS PrivateLink, which allows you to keep all network traffic private, image scanning to detect vulnerabilities, encryption with AWS Key Management Service (KMS) keys, cross-region replication, and lifecycle policies.

11. AWS Lake Formation Data Lake Update

With the addition of row- and cell-level security capabilities, AWS announced new features for allowing safe access to sensitive data in the AWS Lake Formation data lake service.

AWS Lake Formation allows users to aggregate and classify data from databases and object storage, but users must decide how to safeguard access to different slices of data.

And to facilitate that, AWS introduced row- and cell-level security capabilities for Lake Formation which are now generally available. You must have previously had to build and manage several copies of the data, keep all the copies in sync, and handle “complex” data pipelines if you wanted customized access to slices of data. You may now enforce access limits for specific rows and cells using the new enhancements.

12. AWS IoT TwinMaker

AWS IoT TwinMaker is a digital twins solution that allows clients to create cloud-based virtual devices that closely resemble the state and visualization. Through services like AWS IoT SiteWise, Amazon Kinesis Video Streams, and Amazon Simple Storage Service, AWS IoT TwinMaker can ingest data from physical devices. Customers can upload existing 3D models of actual equipment to visualize the digital twin. Multiple 3D models can be combined and placed into a scenario that closely resembles the device topology. Customers may then view a graph that depicts the devices’ interdependence and relationships.

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Preetipadma K
Preetipadma K
Preeti is an Artificial Intelligence aficionado and a geek at heart. When she is not busy reading about the latest tech stories, she will be binge-watching Netflix or F1 races!

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