Amazon S3 on Outposts now supports the ability to monitor your S3 on Outposts events via Amazon CloudWatch. You can now use CloudWatch Events to create a rule for any S3 on Outposts API Event to get notified via all supported CloudWatch targets such as Amazon SNS and Amazon SQS.

You can now assign tags to Amazon Rekognition resources such as collections, stream processors, and Custom Labels models. Tags are key value pairs which enable you to control access to your AWS resources. You can easily identify, classify, or categorize your Amazon Rekognition resources as you create them, giving you a convenient way to ensure that your Amazon Rekognition resources have the right level of access control. You can also assign user-defined cost allocation tags to Amazon Rekognition Custom Labels models, making it easier for you to categorize and track your AWS costs.

AWS Chatbot customers can now receive notifications in chat channels from an expanded range of AWS services through Amazon EventBridge. AWS Chatbot allows users to monitor and interact with AWS resources directly from Slack channels and Amazon Chime chat rooms.  

You now can use AWS CloudTrail to log Amazon DynamoDB Streams data-plane APIs—GetRecords and GetShardIterator—to monitor and investigate item-level changes in your DynamoDB tables. Previously, you could use CloudTrail to log DynamoDB Streams control-plane activity (and not data-plane activity) on your DynamoDB tables.

IT admins and security teams can now view, analyze, and report on the patch compliance of their environment from a single dashboard using Patch Manager. The Patch Manager dashboard provides you with enhanced reporting capabilities and integrates key patching information such as non-compliant instances and instances never scanned for patches into a single pane of glass. It further enables you to troubleshoot issues easily by providing a drilled down view of patch compliance, enabling you to monitor missing patches and analyze execution logs. Using this feature, you can generate patch compliance reports on demand or on a schedule to simplify analysis and sharing of patch compliance information.  

Amazon Textract has achieved Federal Risk and Authorization Management Program (FedRAMP) High authorization, approved by the FedRAMP Joint Authorization Board (JAB), for the AWS GovCloud (US) Regions. Additionally, Amazon Textract is now authorized in the US East (N. Virginia), US East (Ohio), US West (N. California), and US West (Oregon) Regions under FedRAMP Moderate.

Today we are announcing Amazon SageMaker Savings Plans, a new flexible pricing model that allows you to save up to 64% on Amazon SageMaker ML instances in exchange for making a commitment to a consistent amount of eligible usage (e.g. $10/hour) for a 1- or 3-year term. We are also pleased to share that effective April 19th, 2021 we are reducing the prices by up to 14% for instances in Amazon SageMaker. The price reductions apply to instances families ml.t2, ml.t3, ml.m4, ml.m5, ml.m5d, ml.c4, ml.c5, ml.c5d, ml.c5n, ml.r5, ml.r5d, ml.inf1 and ml.g4dn for SageMaker Studio notebooks, SageMaker On-Demand Notebooks, SageMaker Processing, SageMaker Training, SageMaker Real-Time Inference, SageMaker Data Wrangler and SageMaker Batch Transform.

Amazon EMR Release 5.33 now supports 10 new instance types – C6gd, M6gd, R6gd, C6gn,C5a, C5ad, d3, d3en, m5zn and R5b. These instances are supported in all commercial regions where the instances are available. Amazon EMR 5.33 ships with Apache Hudi 0.7.0, ApacheFlink 1.12.1, PrestoDB 0.245 and Zeppelin 0.9.0.

Today we are excited to announce the general availability of EMR Studio, an integrated development environment (IDE) that makes it easy for data scientists and data engineers to develop, visualize, and debug big data and analytics applications written in R, Python, Scala, and PySpark. EMR Studio provides fully managed Jupyter Notebooks, and tools like Spark UI and YARN Timeline Service to simplify debugging. EMR Studio uses AWS Single Sign-On and allows you to log in directly with your corporate credentials without logging into the AWS console.

Today, Amazon Athena announced the general availability of a new capability that makes working with machine learning models as simple as running a SQL query. You can now build and deploy machine learning models in Amazon SageMaker and use SQL functions in Amazon Athena to generate predictions from your SageMaker models. This enables analytics teams to make model-driven insights available to business users and analysts without the need for specialized tools and infrastructure.