Amazon Textract, a machine learning service that makes it easy to extract text and data from any document or image, now offers specialized support to extract data from identity documents, such U.S. Driver Licenses and U.S. Passports. You can extract implied fields like name and address, as well as explicit fields like Date of Birth, Date of Issue, Date of Expiry, ID #, ID Type, and more in the form of key-value pairs. Until today, current OCR based solutions were limited, and did not offer the ability to extract all the required fields accurately due to rich background images or the ability to recognize names and addresses, as well as the fields associated with them (e.g., Washington state ID lists home address with the key “8”), or support ID designs and formats that varied by country or state.

Today, we are excited to announce a new and simplified out-of-box AWS IoT connectivity experience  that can be implemented on two partner-provided FreeRTOS Reference Integration boards: the STM32L4+ and the ESP32-C3. 

Amazon VPC IP Address Manager (IPAM) is a new feature that makes it easier for you to plan, track, and monitor IP addresses for your AWS workloads. With IPAM’s automated workflows, network administrators can more efficiently manage IP addresses.

Amazon VPC Network Access Analyzer is a new feature that enables you to identify unintended network access to your resources on AWS. Using Network Access Analyzer, you can verify whether network access for your Virtual Private Cloud (VPC) resources meets your security and compliance guidelines. With Network Access Analyzer, you can assess and identify improvements to your cloud security posture. Additionally, Network Access Analyzer makes it easier for you to demonstrate that your network meets certain regulatory requirements.

AWS Directory Service for Microsoft Active Directory (AWS Managed Microsoft AD) now helps optimize scaling decisions for improved performance and resilience with Amazon CloudWatch. Starting today, AWS Managed Microsoft AD provides domain controller and directory utilization metrics in Amazon CloudWatch for new and existing directories automatically. Analyzing these utilization metrics helps you quantify your average and peak load times to identify the need for additional domain controllers. With this, you can define the number of domain controllers to meet your performance, resilience, and cost requirements.

AWS Shield Advanced now automatically protects web applications by blocking application layer (Layer 7) DDoS events with no manual intervention needed by you or the AWS Shield Response Team (SRT). When you protect your resources with AWS Shield Advanced and enable automatic application layer DDoS mitigation, Shield Advanced will identify patterns associated with layer 7 DDoS events and isolate this anomalous traffic by automatically creating AWS WAF rules in your web access control lists (ACLs). These rules can be implemented in count mode to observe how they will impact resource traffic and then deployed in block mode. These capabilities enable you to quickly respond to and mitigate DDoS events that threaten the availability of your applications.

Starting today, AWS Transit Gateway supports intra-region peering, giving you the ability to establish peering connections between multiple Transit Gateways in the same AWS Region. With this change, different units in your organization can deploy their own Transit Gateways, and easily interconnect them resulting in less administrative overhead and greater autonomy of operation.

Amazon Simple Queue Service (SQS) announces support of dead-letter queue (DLQ) redrive to source queue, giving you better control over the life cycle of unconsumed messages. Dead-letter queues are an existing feature of Amazon SQS that allows customers to store messages that applications could not successfully consume. You can now efficiently redrive messages from your dead-letter queue to your source queue on the Amazon SQS console. DLQ redrive augments the dead-letter queue management experience for developers and enables them to build applications with the confidence that they can examine their unconsumed messages, recover from errors in their code, and reprocess messages in their dead-letter queues.

Amazon SageMaker now offers enhancements to the machine learning (ML) lineage tracking capability that enables customers to track and query the lineage of artifacts such as data, features, and models across an ML workflow. Now, customers can retrieve the end-to-end lineage graph spanning the entire workflow from data preparation to model deployment through a single query. This feature eliminates undifferentiated heavy lifting needed to retrieve lineage information one workflow step at a time and manually stitch them all together. Customers can also retrieve lineage information for segments of the workflow by defining a step as the focal point and querying the lineage of the steps that are upstream or downstream of that focal point. For instance, customers can define a model as the focal entity and retrieve the location of the raw data set from which features were extracted to train that model.

I had cause to have a closer look at some Arduino inputs – it is that fascinating weather station again. It transpires that some Arduino inputs are better at tolerating slowly-changing inputs than others. For example, ATmega328P (Figure 13-2, Arduino Uno and Nano) and ATmega32U4 (Figure 10-2, Arduino Leonardo, Micro or Pro Micro) Arduinos have hysteresis …

This story continues at Not all Arduino inputs are equal – hunt the Schmidt trigger

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