5/10/2023, 12:00:00 AM ~ 5/11/2023, 12:00:00 AM (UTC)
Recent Announcements
Amazon CodeGuru Security plugin for SageMaker Studio and Jupyter Notebooks now in preview
Amazon CodeGuru Security now supports security and code quality scans for Amazon SageMaker Studio and Jupyter notebooks. This new capability assists notebook users in detecting security vulnerabilities such as injection flaws, data leaks, weak cryptography, or missing encryption within the notebook cells. Users can also detect many common issues that affect the readability, reproducibility, and correctness of computational notebooks, such as misuse of ML library APIs, invalid execution order, and nondeterminism. When vulnerabilities or quality issues are identified in the notebook, CodeGuru generates recommendations that enable users to remediate those issues based on AWS security best practices.
Amazon CloudFront announces one-click security protections
You can now secure your web applications and APIs with AWS WAF with a single click in the Amazon CloudFront console. CloudFront can create and configure out-of-the-box AWS WAF protection for your application as a first line of defense against common web threats. Optionally, you can later configure additional security protections against bots and fraud or other threats specific to your application in the AWS WAF console.
Introducing Cedar, an open-source language for access control
Today, AWS open-sourced the Cedar policy language and authorization engine. You can use Cedar to express fine-grained permissions as easy-to-understand policies enforced in your applications, and you can decouple access control from your application logic. Cedar supports common authorization models such as role-based access control and attribute-based access control. It follows a new verification-guided development process to give you high assurance of Cedar’s correctness and security: AWS formally models Cedar’s authorization engine and other tools, proves safety and correctness properties about them using automated reasoning, and rigorously tests that the model matches the Rust implementation.
AWS Backup now supports AWS User Notifications
Today, AWS Backup is announcing support for managing your backup notifications from the AWS User Notifications console. AWS Backup is a fully managed service that centralizes and automates data protection across AWS services and hybrid workloads. This launch enables you to easily configure, monitor, and manage your notifications related to AWS Backup from a central location.
AWS Lambda now supports AWS X-Ray tracing for SnapStart-enabled functions
You can now use AWS X-Ray to trace and analyze your Lambda functions enabled with Lambda SnapStart. You can use X-Ray traces to gain deeper visibility into your function’s performance and execution lifecycle, helping you identify errors and performance bottlenecks for your latency-sensitive Java applications built using SnapStart-enabled functions.
AWS Systems Manager now allows customers to optimize the compute costs of their applications
Application Manager, a capability of AWS Systems Manager that helps DevOps engineers to investigate and remediate issues in the context of their applications, now enables customers to optimize the cost of compute resources associated with their applications. Customers can now view the cost of their applications in Application Manager and also take recommended actions, such as right sizing instances, to save costs.
AWS announces new AWS Direct Connect location in Lagos, Nigeria
Today, AWS announced the opening of a new AWS Direct Connect location within the Rack Center data center in Lagos, Nigeria. By connecting your network to AWS at the new location, you gain private, direct access to all public AWS Regions (except those in China), AWS GovCloud Regions, and AWS Local Zones.
Amazon SageMaker notebooks now support ml.p4d, ml.p4de and ml.inf1 instances
Amazon SageMaker Studio notebooks and Notebook Instances now support ml.p4d and ml.p4de GPU-based instances that provide the best performance for interactive machine learning (ML) workloads in the cloud for applications such as large language models with billions of parameters, natural language processing, object detection and classification, seismic analysis, genomics research, and more. These instances are powered by the latest Intel® Cascade Lake processors and eight NVIDIA A100 Tensor Core GPUs.
Announcing Provisioned Concurrency for Amazon SageMaker Serverless Inference
Today, we are excited to announce general availability of Provisioned Concurrency support for Amazon SageMaker Serverless Inference. Provisioned Concurrency allows you to deploy models on serverless endpoints with predictable performance and high scalability. You can add provisioned concurrency to your serverless endpoints, and for the pre-defined amount of provisioned concurrency SageMaker will keep the endpoints warm and ready to respond to requests instantaneously. Provisioned Concurrency is ideal for customers who have predictable traffic, with low throughput.
Amazon SNS now supports faster automatic deletion of unconfirmed subscriptions
Amazon Simple Notification Service (Amazon SNS) now supports automatic deletion of unconfirmed subscriptions once they have been in a pending confirmation state for 48 hours. This reduces the time to delete your unconfirmed subscriptions from the previous 72 hour period. This applies to all new subscriptions and does not require any on-boarding.
Amazon MemoryDB for Redis now supports IAM Authentication
Amazon MemoryDB for Redis now supports AWS Identity and Access Management (IAM) authentication access to its clusters. With this launch, you can associate IAM users and roles with MemoryDB users and manage their cluster access.
Amazon SageMaker Canvas can now operationalize ML models in production
You can now register machine learning (ML) models built in Amazon SageMaker Canvas with a single click to SageMaker Model registry enabling you to operationalize ML models in production. SageMaker Canvas is a visual interface that enables business analysts to generate accurate ML predictions on their own — without requiring any ML experience or having to write a single line of code.
Amazon MemoryDB for Redis adds support for Redis 7
Amazon MemoryDB for Redis now supports Redis 7. This release brings several new features to MemoryDB:\n
Redis Functions: MemoryDB adds support for Redis Functions, and provides a managed experience enabling developers to execute LUA scripts with application logic durably stored on the MemoryDB cluster.
ACL improvements: MemoryDB adds support for the next version of Redis Access Control Lists (ACLs). With MemoryDB, clients can now specify multiple sets of permissions on specific keys or keyspaces in Redis.
Sharded Pub/Sub: MemoryDB now gives you the ability to run Redis’ Pub/Sub functionality in a sharded way. With MemoryDB, channels are bound to a shard in the MemoryDB cluster, eliminating the need to propagate channel information across shards resulting in improved scalability.
Enhanced I/O Multiplexing: MemoryDB now includes enhanced I/O multiplexing, which delivers significant improvements to throughput and latency at scale. As an example, when using r6g.4xlarge node and running 5200 concurrent clients, you can achieve up to 46% increased throughput (read and write operations per second) and up to 21% decreased P99 latency, compared with MemoryDB for Redis 6.
AWS announces new AWS Direct in Atlanta Georgia
Today, AWS announced the opening of a new AWS Direct Connect location within the QTS Atlanta DC1 data center in Atlanta, Georgia. By connecting your network to AWS at the new location, you gain private, direct access to all public AWS Regions (except those in China), AWS GovCloud Regions, and AWS Local Zones.
Private Access to the AWS Management Console is generally available
Today, AWS announces the general availability of AWS Management Console Private Access. Private Access is a new security feature that allows customers to limit access to the AWS Management Console from their Virtual Private Cloud (VPC) or connected networks to a set of trusted AWS accounts and organizations.
SageMaker Autopilot supports training ML models with weights, eight additional objective metrics
Amazon SageMaker Autopilot, a low-code machine learning (ML) service which automatically builds, trains and tunes the best ML models, now supports training with weighted objective metrics in Ensemble mode and also supports eight additional objective metrics. Assigning weights to each data sample in the training data set can improve overall model performance by helping the model learn better, reduce bias towards a particular class, and increase stability.
AWS Blogs
AWS Japan Blog (Japanese)
- Using AWS Inferentia2 and AWS Trainium on Amazon SageMaker to Realize Low-Cost, High-Performance Generative AI Inference
- Amazon ECS task definitions can now be deleted
AWS Japan Startup Blog (Japanese)
AWS Open Source Blog
- Using Open Source Cedar to Write and Enforce Custom Authorization Policies
- Announcing Snapchange: An Open Source KVM-backed Snapshot Fuzzing Framework
AWS Architecture Blog
AWS Big Data Blog
- Amazon OpenSearch Service Under the Hood: Multi-AZ with Standby
- Perform secure database write-backs with Amazon QuickSight
AWS Compute Blog
- Debugging SnapStart-enabled Lambda functions made easy with AWS X-Ray
- Implementing cross-account CI/CD with AWS SAM for container-based Lambda functions
AWS Contact Center
AWS Database Blog
- A framework for Amazon DynamoDB Transactions
- Supply chain data analysis and visualization using Amazon Neptune and the Neptune workbench
AWS for Industries
AWS Machine Learning Blog
- Operationalize ML models built in Amazon SageMaker Canvas to production using the Amazon SageMaker Model Registry
- Amazon SageMaker with TensorBoard: An overview of a hosted TensorBoard experience
- Reduce Amazon SageMaker inference cost with AWS Graviton
- How Sleepme uses Amazon SageMaker for automated temperature control to maximize sleep quality in real time
- Publish predictive dashboards in Amazon QuickSight using ML predictions from Amazon SageMaker Canvas
- Announcing new Jupyter contributions by AWS to democratize generative AI and scale ML workloads
- Schedule your notebooks from any JupyterLab environment using the Amazon SageMaker JupyterLab extension
AWS for M&E Blog
AWS Security Blog
- Detect threats to your data stored in RDS databases by using GuardDuty
- Customer checklist for eIDAS regulation now available