Amazon Chime SDK Voice Connector audio streaming now supports G.711 A-law encoded audio. With this update, companies can stream audio to AWS from their phone systems using session initiation protocol recording (SIPREC) with G.711 A-law encoded audio. Businesses use Amazon Chime SDK Voice Connector audio streaming to send call audio from their phone systems to AWS for real-time and post-call analytics, machine learning (ML) and natural language processing, call recording, and other business processes. This helps businesses build analytics and compliance solutions that help improve customer experiences using real-time ML-powered conversation insights derived from their streamed phone calls. G.711 A-law encoded audio streaming is available in all AWS regions where Amazon Chime SDK Voice Connector audio streaming is available. To get started or to learn more about Amazon Chime SDK Voice Connector streaming, refer to the Administrator Guide or visit the Amazon Chime SDK website.

Amazon Connect Contact Lens now provides managers with generative AI-powered recommendations for answers to questions in agent evaluation forms, enabling them to perform evaluations faster and more accurately. Managers now receive additional agent behavioral insights (e.g., did the agent show empathy while delivering bad news?) and will get context and justification for the recommended answers (reference points from the transcript that were used to provide answers). These generative AI-powered recommendations are built using Amazon Bedrock and are available in US West (Oregon) and US East (North Virginia) regions in the English language. To learn more, please visit our documentation and our webpage. During preview, this feature is included with Contact Lens performance evaluations at no additional charge. For information about Contact Lens pricing, please visit our pricing page.

Today, Amazon Personalize announces the general availability of two new recipes, User-Personalization-v2 and Personalized-Ranking-v2 (v2 recipes). Built on Transformers architecture, these new recipes support catalogs with up to 5 million items with lower inference latency. Amazon Personalize testing showed that v2 recipes improved recommendation accuracy by up to 9% and recommendation coverage by up to 1.8x compared to previous versions. A higher coverage means Amazon Personalize recommends more of your catalog. These new recipes also support item metadata like genres and descriptions in inference responses, allowing customers to easily enrich recommendations in their user interfaces. Amazon Personalize enables customers to personalize their website, app, emails, and more, using the same machine learning (ML) technology used by Amazon, without requiring any ML expertise. Using recipes – algorithms for specific uses cases – provided by Amazon Personalize, customers can deliver a wide array of personalization, including product or content recommendations and personalized ranking. To get started with Amazon Personalize, provide an activity stream – clicks, page views, signups, purchases, and so forth – as well as a catalog of the items you want to recommend, such as videos, products, articles, or music. You can also provide demographic information from your users. Amazon Personalize will process the data, train and optimize your custom model, then host it for your applications. User-Personalization-v2 and Personalized-Ranking-v2 are available in all supported regions. You can access them through Amazon Personalize console or API. To get started, refer to our documentation.

Today, AWS announces the general availability of file commit history in Amazon CodeCatalyst. Customers can now view the file git commit history in the CodeCatalyst’s console. Amazon CodeCatalyst helps teams plan, code, build, test, and deploy applications on AWS. Viewing the history of commits, pertaining to a file, is a way to reduce the effort for developers when trying to understand the history of changes for a codebase. To learn more, check out our documentation on or visit our webpage.

AWS Trusted Advisor introduces new API to enable you to programmatically exclude resources from recommendations associated with Trusted Advisor best practice checks. On Nov 17 2023, Trusted Advisor announced the launch of new APIs available to Business, Enterprise On-Ramp, or Enterprise Support customers. This launch adds the BatchUpdateRecommendationResourceExclusions API to this suite of APIs to allow users to exclude specific resources from specific checks programmatically. This capability is only available to via AWS Trusted Advisor APIs, and not available through AWS Support API (SAPI).  Trusted Advisor continuously evaluates your AWS environment using best practice checks in the categories of cost optimization, performance, resilience, security, operational excellence, and service quotas, and provides recommendations to remediate any deviations from best practices. Based on your business and architectural context, you can now use this exclude capability within your operational tooling to increase the relevance of recommendations shown by AWS Trusted Advisor. Trusted Advisor APIs are generally available in the US East (Ohio), US East (N. Virginia), US West (Oregon), Asia Pacific (Seoul), Asia Pacific (Sydney), and Europe (Ireland) Regions. To learn more about Trusted Advisor API, please refer to the user guide.

Beginning today, customers can use Amazon Bedrock in the Asia Pacific (Mumbai) region to easily build and scale generative AI applications using a variety of foundation models (FMs) as well as powerful tools to build generative AI applications.

Amazon Bedrock is a fully managed service that offers a choice of high-performing large language models (LLMs) and other FMs from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, as well as Amazon via a single API. Amazon Bedrock also provides a broad set of capabilities customers need to build generative AI applications with security, privacy, and responsible AI built in. These capabilities help you build tailored applications for multiple use cases across different industries, helping organizations unlock sustained growth from generative AI while ensuring customer trust and data governance.

To get started, visit the Amazon Bedrock page and see the Amazon Bedrock documentation for more details.

AWS B2B Data Interchange now automatically generates 997 functional acknowledgements and TA1 interchange acknowledgements in response to all relevant inbound X12 electronic data interchange (EDI) transactions. These acknowledgements are used to confirm receipt of individual transactions and to report errors. With this launch, you can now automate delivery of 997 and TA1 acknowledgements to trading partners that require them. Each acknowledgement generated by AWS B2B Data Interchange is stored in Amazon S3, alongside your transformed EDI, and emits an Amazon EventBridge event. You can use these events to automatically send the acknowledgement generated by AWS B2B Data Interchange to your trading partners via SFTP or AS2 using AWS Transfer Family or any other EDI connectivity solution. Support for automated acknowledgements is available in all AWS Regions where AWS B2B Data Interchange is available and provided at no additional cost. To get started with AWS B2B Data Interchange for building and running your event-driven EDI workflows, take the self-paced workshop or visit the documentation.

The Poles are the biggest vaccine sceptics in Europe. 31 percent of people surveyed in Poland as part of Statista’s Consumer Insights survey say they have little confidence in the …

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96 years ago this propeller-driven passenger transport link was opened. The  idea was that it would carry passengers above the rail lines moving them more quickly than the slower freight …

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Renesas has increased support for its first RISC-V-based general-purpose microcontrollers by integrating Segger’s Embedded Studio into version 1.1.0 of Renesas’ RISC-V Smart Configurator code generator for R9A02G021 group MCUs. Smart …

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