CTT Big4 News AWS Recently #AWS019

CTT Big4 News AWS Recently #AWS019

Hello Friends, welcome to Big4 Recently

AWS, IBM, Google & Azure – the Big4 Cloud Service providers. In this article, we will explore latest AWS Recently

Amazon CloudFront is launched in six new Edge locations

Amazon CloudFront announces six new Edge locations, across four continents. In the United States, the new locations are in Chicago, Newark, and Ashburn. Internationally, the new locations are in Munich, Tokyo, and Rio de Janerio. Just over one year ago, AWS announced our 100th Edge location in Tokyo. The addition of these six new locations today now brings CloudFront’s total network to 150 Points of Presence worldwide, across 65 cities and 29 countries.

Amazon CloudFront announces support for Origin Failover

Starting today, you can enable Origin Failover for your Amazon CloudFront distributions to improve the availability of content delivered to your end users.

With CloudFront’s Origin Failover capability, you can setup two origins for your distributions – primary and secondary, such that your content is served from your secondary origin if CloudFront detects that your primary origin is unavailable. CloudFront already allows you to configure custom error pages or generate redirects with Lambda@Edge if your origin is unavailable. Now with Origin Failover, you can easily setup failover logic between combinations of AWS origins or non-AWS custom HTTP origins such that there is minimal interruption to your viewer’s experience. For example, you can have two Amazon S3 buckets that serve as your origin, that you independently upload your content to. If an object that CloudFront requests from your primary bucket is not present or if connection to your primary bucket times-out, CloudFront will request the object from your secondary bucket. So, you can configure CloudFront to trigger a failover in response to either HTTP 4xx or 5xx status codes.

Amazon CloudFront announces support for the WebSocket protocol

You can now use Amazon CloudFront for applications using the WebSocket protocol to provide improved performance and security to your end users.

WebSocket is a real-time communication protocol that provides bidirectional communication between a client (such as a browser) and a server over a long-held TCP connection. By using a persistent open connection, the client and the server can send real-time data to each other without the client having to frequently reinitiate connections checking for new data to exchange. WebSocket connections are often used in chat applications, collaboration platforms, multiplayer games, and financial trading platforms. 

Amazon Neptune Now Supports HTTPS for Encrypted Client Connections

Amazon Neptune now allows you to use HTTPS to encrypt data in transit between your graph database clients and the Neptune service endpoints. This enhancement uses the industry-standard Transport Layer Security (TLS) 1.2 protocol to encrypt all data sent to and from connected clients.

Now you can connect to Neptune’s Gremlin server and SPARQL 1.1 Protocol REST endpoints using HTTPS. You don’t need to manage your own certificates. Neptune automatically provides SSL certificates for your Neptune database instances. You can see how to connect using HTTPS in the latest Amazon Neptune documentation.

Monitor and Visualize Training Metrics of your Machine Learning Models with Amazon SageMaker and Amazon CloudWatch

Amazon SageMaker can now publish training metrics to Amazon CloudWatch in real-time. You can then use CloudWatch to query, monitor, and visualize your Amazon SageMaker training jobs.

Model training is an important process aimed at enabling the model to predict the required outcomes of your business requirements. As part of the training process, machine learning algorithms produce metrics such as training loss and validation accuracy. These metrics help you understand if the model is learning well and where additional tuning is needed. With this new enhancement, you can publish these metrics to AWS CloudWatch. Once published, you can then visualize the metrics in the ClouldWatch console and query them using both SageMaker APIs and CloudWatch APIs.

Amazon SageMaker is now integrated with Apache Airflow

Amazon SageMaker is now integrated with Apache Airflow for building and managing your machine learning workflows. With this integration, multiple SageMaker operators including model training, hyperparameter tuning, model deployment, and batch transform are now available with Airflow.

Apache Airflow is an open-source platform to programmatically author, schedule, and monitor workflows that can be deployed in the cloud or on-premises. Airflow uses operators to represent tasks that are going to be executed in a workflow. SageMaker joins other AWS services such as Amazon S3, Amazon EMR, AWS Batch, AWS Redshift, and many others as contributors to Airflow with different operators.

That’s it friends in this week for AWS Recently, however please visit specific Cloud provider news that you are interested in from below links.

Author: Debashree

A Technical writer and passionate about digital life. Always eager to learn and share knowledge.

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