As new data privacy regulations like GDPR (General Data Protection Regulation, 2017) have come into effect, customers are under increased pressure to monetize media assets while abiding by the new rules. Monetizing media while respecting privacy regulations requires the ability to automatically extract granular metadata from assets like text, images, video, and audio files at internet scale. It also requires a scalable way to map media assets to industry taxonomies that facilitate discovery and monetization of content. This use case is particularly significant for the advertising industry as data privacy rules cause a shift from behavioral targeting using third-party cookies. Third-party cookies help enable personalized ads for web users, and allow advertisers to reach their intended audience.

Regulatory mandates, audit requirements, and security policies often call for data visibility and granular data control while using Amazon Simple Storage Service (Amazon S3) for shared datasets. Because data on Amazon S3 is often accessible by multiple applications and teams, fine-grained access controls should be implemented to restrict privileged information such as personally identifiable information (PII) to only authorized entities. For example, PII data used by a marketing application may need to be masked to meet data privacy requirements. Similarly, an order inventory dataset used by a production ordering application may include customer credit card information that shouldn't be accessed by a business analytics application, so this data should be suppressed to prevent unintended data leakage. In this post, we show you how to implement Amazon S3 Object Lambda to process and modify data retrieved from Amazon S3.

Unstructured data belonging to the enterprise continues to grow, making it a challenge for customers and employees to get the information they need. Amazon Kendra is a highly accurate intelligent search service powered by machine learning (ML). It helps you easily find the content you're looking for, even when it's scattered across multiple locations and content repositories. Amazon Kendra leverages deep learning and reading comprehension to deliver precise answers. It offers natural language search for a user experience that's like interacting with a human expert.

Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to analyze text documents and identify insights such as sentiment, entities, and topics from text. You can use Custom Entity Recognition to identify terms that are specific to your domain. For example, you can instantly extract product names, financial entities or any term relevant to you from unstructured text documents. Starting today, Amazon Comprehend is adding support for the following five new languages to Custom Entity Recognition: French, German, Italian, Portuguese, and Spanish.

Amazon Comprehend now supports Amazon Virtual Private Cloud (Amazon VPC) endpoints via AWS PrivateLink so you can securely initiate API calls to Amazon Comprehend from within your VPC and avoid using the public internet. Amazon Comprehend is a fully managed natural language processing (NLP) service that uses machine learning (ML) to find meaning and insights in text. You can use Amazon Comprehend to analyze text documents and identify insights such as sentiment, people, brands, places, and topics in text. Using AWS PrivateLink, you can access Amazon Comprehend easily and securely by keeping your network traffic within the AWS network, while significantly simplifying your internal network architecture. It enables you to privately access Amazon Comprehend APIs from your VPC in a scalable manner by using interface VPC endpoints.

Amazon Textract is a machine learning (ML) service that makes it easy to extract text and data from scanned documents. Textract goes beyond simple optical character recognition (OCR) to identify the contents of fields in forms and information stored in tables. This allows you to use Amazon Textract to instantly "read" virtually any type of document and accurately extract text and data without needing any manual effort or custom code. Amazon Textract has multiple applications in a variety of fields. For example, talent management companies can use Amazon Textract to automate the process of extracting a candidate's skill set.

Amazon Comprehend is a fully managed natural language processing (NLP) service that enables text analytics to extract insights from the content of documents. Amazon Comprehend supports custom classification and enables you to build custom classifiers that are specific to your requirements, without the need for any ML expertise. Previously, custom classification supported multi-class classification, which is used to assign a single label to your documents from a list of mutually exclusive labels. Starting January 6, custom classification also supports multi-label classification. With multi-label classification you can train models and classify your documents with more than one label.

If you're like most companies, you wish to better understand your customers and your brand image. You'd like to track the success of your marketing campaigns, and the topics of interest--or frustration--for your customers. Social media promises to be a rich source of this kind of information, and many companies are beginning to collect, aggregate, and analyze the information from platforms like Twitter. However, more and more social media conversations center around images and video; on one recent project, approximately 30% of all tweets collected included one or more images. These images contain relevant information that is not readily accessible without analysis.

This months meetup is all about using AWS AI services and custom Machine Learning models to power your web applications! Mike Apted, Startup Solutions Architect with Amazon Web Services, is back presenting for this months meetup! RSVP ASAP and we'll see you there! Agenda: 6:00pm - Arrival, mingling, pizza eating 6:20pm - Welcome & Introductions 6:30pm - Presentation Begins 7:20pm - Q&A and Open Group Discussions 8:00pm - Event concludes Presentation Title: Using AWS AI services and custom ML models to power your web applications Presentation Summary: In this session, we will look at how you can build a brand new web application to do speech to text generation, translate text, gain insights from text, convert text to speech, and to detect objects via the Amazon Transcribe, Amazon Translate, Amazon Comprehend, Amazon Polly and Amazon Rekognition respectively. We will then use Amazon SageMaker to label, train and deploy our own model against which we will make predictions from the web application.

We're excited to announce an end-to-end solution that leverages natural language processing to analyze and visualize unstructured text in your Amazon Elasticsearch Service domain with Amazon Comprehend in the AWS Cloud. You can deploy this solution in minutes with an AWS CloudFormation template and visualize your data in a Kibana dashboard. Amazon Elasticsearch Service (Amazon ES) is a fully managed service that delivers Elasticsearch's easy-to-use APIs and real-time capabilities along with the availability, scalability, and security required by production workloads. Amazon Comprehend is a fully managed natural language processing (NLP) service that enables text analytics to extract insights from the content of documents. Customers can now leverage Amazon ES and Amazon Comprehend to index and analyze unstructured text, and deploy a pre-configured Kibana dashboard to visualize extracted entities, key phrases, syntax, and sentiment from their documents.