Now in preview, AWS Glue Elastic Views is a new capability of AWS Glue that makes it easy to build materialized views that combine and replicate data across multiple data stores without you having to write custom code. With AWS Glue Elastic Views, you can use familiar Structured Query Language (SQL) to quickly create a virtual table—a materialized view—from multiple different source data stores. AWS Glue Elastic Views copies data from each source data store and creates a replica in a target data store. AWS Glue Elastic Views continuously monitors for changes to data in your source data stores, and provides updates to the materialized views in your target data stores automatically, ensuring data accessed through the materialized view is always up-to-date.

Contact Lens for Amazon Connect , launched at re:Invent 2019, provides a set of machine learning (ML) capabilities integrated into Amazon Connect that analyze call recordings or customer sentiment, trends, and compliance of conversations. Now, Contact Lens supports real-time call analytics capabilities, enabling you to detect customer issues during live calls and resolve them faster.

Amazon Lookout for Vision is a new machine learning service that enables you to find visual defects in industrial products, accurately and at scale. With Lookout for Vision you can automate real-time visual inspection with computer vision for processes like quality control and defect assessment – with no machine learning expertise required. It uses computer vision to identify missing components in products, damage to vehicles or structures, irregularities in production lines, and even miniscule defects in silicon wafers — or any other physical item where quality is important.  

Today, we are excited to announce Amazon Lookout for Equipment, a service which provides customers with existing sensors on their industrial equipment, a way to send their sensor data to AWS to build machine learning models for them and return predictions to detect abnormal equipment behavior. This enables predictive maintenance that allows them to take action before machine failures occur and avoid unplanned downtime. 

Amazon Connect Tasks improves agent productivity and lowers costs by empowering contact center managers to prioritize, assign, and track customer service tasks across the disparate applications used by agents. This includes tasks like following up on a case in a Customer Relationship Management (CRM) application such as Salesforce, or processing a claim in a business-specific application. You can also automate repetitive work by creating contact flows in Amazon Connect to automate action items that don’t require agent interaction (e.g., notifying a customer of an update on their case), so that agents can focus more of their time interacting with customers.

Today we are announcing preview of AWS Local Zones in Boston, Houston, and Miami, with plans to launch 12 additional AWS Local Zones throughout 2021 in key metro areas in the United States including Atlanta, Chicago, and New York. Using these new AWS Local Zones, customers will now be able to deliver ultra-low latency applications to end-users in cities across the continental United States.

We are excited to announce Amazon Monitron, an end-to-end system that uses machine learning (ML) to detect abnormal behavior in industrial machinery, enabling you to implement predictive maintenance and reduce unplanned downtime.

Amazon Connect Customer Profiles equips contact center agents with a more unified view of a customer’s current situation to provide more personalized customer service. Customer Profiles automatically brings together customer information from multiple applications into a unified customer profile, delivered to agents at the beginning of the customer interaction.

Today, we are excited to announce a new capability in Amazon QuickSight called Amazon QuickSight Q. Q is a machine learning-powered natural language capability that empowers business users to ask questions about all of their data using everyday business language and get answers in seconds. For example, users simply type “what is our year-over-year growth rate” and get an instant answer in QuickSight as a visualization.  

AWS Lambda reduced the billing granularity for Lambda function duration from 100ms down to 1ms. This will lower the price for most Lambda functions, more so for short duration functions. Their compute duration will be billed in 1ms increments instead of being rounded up to the nearest 100 ms increment per invoke. For example, a function that runs in 30ms on average used to be billed for 100ms. Now, it will be billed for 30ms resulting in a 70% drop in its duration spend.