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. 

You can now use AWS Lambda with extensions for your favorite operational tools for monitoring, observability, security, and governance. Today, you can use extensions for the following tools: AppDynamics, Check Point, Datadog, Dynatrace, Epsagon, HashiCorp, Lumigo, New Relic, Thundra, Splunk, AWS AppConfig, and Amazon CloudWatch Lambda Insights.

AWS Compute Optimizer now analyzes additional Amazon EBS metrics to generate enhanced EC2 instance type recommendations. Enhanced recommendations are now available for Compute Optimizer and Cost Explorer Rightsizing Recommendations customers.

Whisper flows, which allow information to be passed to an agent or customer while being connected on a call, are now available in Amazon Connect chat. For example, you can display text showing the customer’s name and membership status to an agent, inform a customer that the chat is being recorded for quality assurance purposes, or provision access to a customer relationship management system for the agent accepting the contact. Whisper flows allow you to create personalized, one-sided interactions, which only an agent or end-customer will see, by adding the ‘set whisper flow’ block to your Amazon Connect contact flow. 

Thirty-two new or updated datasets from the Massachusetts Institute of Technology, the First Street Foundation, Ookla, and others are available on the Registry of Open Data in the following categories. 

Starting today, you can create multilevel hierarchies with AWS Cost Categories to group your cost and usage information based on your business needs. Cost Category is added as a new dimension, in addition to Tag, Account, Service, and Charge Type. Furthermore, you can now track whether your most recent cost category updates have been applied to your cost and usage data via a new “status” attribute on your Cost Categories dashboard.