Amazon Rekognition content moderation is a deep learning-based service that can detect inappropriate, unwanted, or offensive images and videos, making it easier to find and remove such content at scale. Amazon Rekognition provides a detailed taxonomy of moderation categories such as ‘Explicit Nudity’, ‘Suggestive’, ‘Violence’, and ‘Visually Disturbing’. Starting today, customers can detect 6 new categories – ‘Drugs’, ‘Tobacco’, ‘Alcohol’, ‘Gambling’, ‘Rude Gestures’, and ‘Hate Symbols’. In addition, customers also get improved detection rates for already supported categories. Using Amazon Rekognition moderation APIs, social media, broadcast media, advertising, and e-commerce customers can create a better user experience, provide brand safety assurances to advertisers, or comply with local and global regulations. 

Amazon CloudWatch Application Insights, working closely with customers, has introduced an enhanced user interface to make setting up and managing your enterprise application monitoring even more straight forward. CloudWatch Application Insights is a capability that helps enterprise customers easily setup application monitoring and enhanced observability for AWS resources. The user interface improvements streamline the steps for doing this in a more intuitive and consistent approach. 

Amazon EMR now allows you to leverage AWS Lake Formation for defining and enforcing fine-grained access control policies for Apache Spark applications. Previously, this feature was in beta.  

Amazon Transcribe is an automatic speech recognition (ASR) service that you can use to add speech-to-text capabilities to your applications. Starting today, AWS customers can use AWS PrivateLink to access the Amazon Transcribe batch API from their Amazon Virtual Private Cloud (Amazon VPC) without using public IPs or requiring the traffic to traverse the Internet. AWS PrivateLink provides private connectivity between VPCs and AWS services, without ever leaving the Amazon network. With this launch, AWS PrivateLink is now supported for both Batch and Streaming APIs.  

Starting today, you can use easily restore a new Amazon RDS for MySQL database instance from a backup of your existing MySQL 8.0 database, whether it’s running on Amazon EC2 or outside of AWS. This is done by using Percona XtraBackup to create a backup of your existing MySQL database, uploading the resulting files to an Amazon S3 bucket, and then creating a new Amazon RDS DB instance through the RDS Console or AWS Command Line Interface (CLI).

Amazon Rekognition Custom Labels is an automated machine learning (AutoML) feature that allows customers to find objects and scenes in images, unique to their business needs, with a simple inference API. Customers can create a custom ML model simply by uploading labeled images. No ML expertise is required.  

We are excited to announce efficiency improvements for Amazon Personalize that decrease the time required to train models by up to 40% and reduce the latency for generating real-time recommendations by up to 30%. Amazon Personalize enables developers to build applications with the same machine learning (ML) technology used by Amazon.com for real-time personalized recommendations – no ML expertise required. Amazon Personalize provisions the necessary infrastructure and manages the entire ML pipeline, including processing the data, identifying features, using the best algorithms, and training, optimizing, and hosting the models.

You can now discover endpoints registered in AWS Cloud Map with optional parameters that filter the returned results only when there is a matching custom attribute. AWS Cloud Map is a cloud resource discovery service. Using AWS Cloud Map, you can define custom names for your application resources, such as Amazon EC2 instances, Amazon ECS tasks, Amazon S3 buckets, or any other cloud resource. Your application can then discover the location and metadata of cloud resources associated with these custom names via AWS SDK or by making authenticated API calls.

You can now configure your Amazon Lex chat bot to improve engagement with customers who speak US Spanish. Amazon Lex allows you to create intelligent conversational chatbots that can be used with Amazon Connect to automate high volume interactions without compromising customer experience. Customers can perform tasks such as changing a password, requesting a balance on an account, or scheduling an appointment using natural conversational language. Customers can say things like “I need help with my device” instead of having to listen through and remember a list of options like 1 for sales, or 2 for appointments.  

Amazon SageMaker Studio is now available in the Europe (Frankfurt) AWS region. Amazon SageMaker Studio is the first fully integrated development environment (IDE) for machine learning (ML). SageMaker Studio provides a single, web-based visual interface where you can perform all ML development steps, giving you complete access, control, and visibility required to build, train, and deploy models. Within the unified SageMaker Studio visual interface, you can perform all ML development activities including notebooks, experiment management, automatic model creation, debugging, and model drift detection.