8/12/2022, 12:00:00 AM ~ 8/15/2022, 12:00:00 AM (UTC)

Recent Announcements

Amazon Personalize allows rule-based promotions in recommendations

Amazon Personalize now allows you to promote specific items in all users’ recommendations based on rules that align with your business goals. Amazon Personalize enables developers to improve customer engagement through personalized product and content recommendations – no ML expertise required. Personalize currently recommends relevant items for each user or similar items to a given item based on interactions and metadata information. With this launch, you can define business rules to identify items that you want to promote across your user-base. You can also control the percentage of promoted content in your recommendations. For example, E-commerce customers can use this feature to fill 20% of recommendations with items marked as “on sale”. Similarly, Video-on-Demand customers can use this feature to fill 40% of their rail/carousel with linear/live TV content. Based on your business rules and definitions, Amazon Personalize finds relevant items for promotion and distributes them within user-specific recommendations.

AWS Graviton2-based Amazon EC2 M6g, M6gd, C6g, and R6g are now available in Asia Pacific (Jakarta) region

Starting today, Amazon EC2 M6g, M6gd, C6g, and R6g instances powered by AWS Graviton2 processors are available in Asia Pacific (Jakarta) region. AWS Graviton2-based instances provide up to 40% better price performance over comparable x86-based instances. M6g and M6gd instances are ideal for general purpose workloads such as application servers, microservices, mid-size data stores, and caching fleets. C6g instances are ideal for compute-intensive workloads such as high performance computing (HPC), batch processing, ad serving, video encoding, gaming, distributed analytics, and CPU-based machine learning. The local SSD storage provided on M6gd instances is ideal for applications that need access to high-speed, low latency storage of data such as caching, logs, and scratch files. R6g instances are ideal for memory-intensive applications such as open-source databases, in-memory caches, and real time big data analytics.

AWS DeepRacer Student now offers community races and new Intel learning content with a look under the hood of the DeepRacer vehicle

AWS DeepRacer Student, presented by Intel, is the first global autonomous racing league for students offering free educational material and resources to get hands on with machine learning (ML) by powering a 1/18th scale race car driven by reinforcement learning. Now available are student community races and additional learning content diving into the OpenVINO™ toolkit by Intel on machine learning inference.

The Amazon Chime SDK announces elastic channels

The Amazon Chime SDK enables developers to add intelligent real-time audio, video, and screen share to their web and mobile applications. Starting today, Amazon Chime SDK messaging supports large-scale chat experiences for up to one million users with elastic channels. Use cases include watch parties for sporting events, political events, or live entertainment with create elastic channels. Elastic channels help make it easy for you to create secure, scalable, moderated chat experiences for large audiences which you can use with your built in moderation features to help enforce brand, corporate, or community guidelines.

Amazon Personalize now supports incremental bulk dataset imports

We are excited to announce that Amazon Personalize now supports incremental bulk dataset imports; a new option for updating your data and improving the quality of your recommendations. Amazon Personalize enables developers to improve customer engagement through personalized product and content recommendations – no ML expertise required. Previously, Amazon Personalize supported two types of data ingestion: full bulk dataset imports for ingesting large datasets, and APIs (PutEvents, PutItems, PutUsers) for real-time ingestion. Now you can keep your existing datasets intact and add new bulk-loaded data without resupplying your entire dataset. Amazon Personalize will automatically update records with the current version if your incremental import duplicates any records found in your existing dataset, further simplifying the data ingestion process.

Amazon Kinesis Data Analytics is now available in Asia Pacific (Jakarta) region

Amazon Kinesis Data Analytics is now available in the Asia Pacific (Jakarta) region.

Amazon RDS Custom for Oracle now supports Scale Compute

Amazon Relational Database Service (Amazon RDS) Custom for Oracle now supports Scale Compute operation. With this, you can now scale your Amazon RDS Custom for Oracle instance up or down.

Amazon Personalize launches support for range queries on dynamic filters

Amazon Personalize now supports comparison operators ( =, <, <=, >, >=) for dynamic filters to provide flexibility in applying your business rules to recommendations. Amazon Personalize enables developers to improve customer engagement through personalized product and content recommendations – no ML expertise required. Dynamic filters currently  allow you to change filter criteria at the time of getting recommendations, so that you can modify filter rules on the fly without having to create separate permutations. With this launch, you get even more control over your recommendations by having the option to use comparison operators ( =, <, <=, >, >=) with dynamic filter values. For example, video or news websites can use this feature in a “What’s New” section to only recommend items introduced in the last 7 days. Similarly, retail customers can upsell items on their checkout page by filtering for items priced higher than the item(s) currently in the shopping cart. This feature builds on existing dynamic filter functionality by providing increased flexibility in applying your individual business rules, enabling you to modify recommendations based on users’ needs, preferences, and changing behavior.

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