Amazon Kendra is an intelligent search service powered by machine learning (ML), enabling organizations to provide relevant information to customers and employees, when they need it. Amazon Kendra uses ML algorithms to enable users to use natural language queries to search for information scattered across multiple data souces in an enterprise, including commonly used document storage systems like Microsoft OneDrive. OneDrive is an online cloud storage service that allows you to host your content and have it automatically sync across multiple devices. We're excited to announce that we have updated the OneDrive connector for Amazon Kendra to add even more capabilities. For example, we have added support to search OneNote documents.

When you deploy intelligent search in your organization, two important factors to consider are access to the latest and most comprehensive information, and a contextual discovery mechanism. Many companies are still struggling to make their internal documents searchable in a way that allows employees to get relevant information knowledge in a scalable, cost-effective manner. A 2018 International Data Corporation (IDC) study found that data professionals are losing 50% of their time every week--30% searching for, governing, and preparing data, plus 20% duplicating work. Amazon Kendra is purpose-built for addressing these challenges. Amazon Kendra is an intelligent search service that uses deep learning and reading comprehension to deliver more accurate search results.

Medical data is highly contextual and heavily multi-modal, in which each data silo is treated separately. To bridge different data, a knowledge graph-based approach integrates data across domains and helps represent the complex representation of scientific knowledge more naturally. For example, three components of major electronic health records (EHR) are diagnosis codes, primary notes, and specific medications. Because these are represented in different data silos, secondary use of these documents for accurately identifying patients with a specific observable trait is a crucial challenge. By connecting those different sources, subject matter experts have a richer pool of data to understand how different concepts such as diseases and symptoms interact with one another and help conduct their research.

These types of workspaces are rich with data and contain sets of knowledge and information that can be a great source of truth to answer organizational questions. Unfortunately, it isn't always easy to tap into these data sources to extract the information you need. For example, the data source might not be connected to an enterprise search service within the organization, or the service is outdated and lacks natural language search capabilities, leading to poorer search experiences. Amazon Kendra is an intelligent search service powered by machine learning (ML). Amazon Ken dra reimagines enterprise search for your websites and applications so your employees and customers can easily find the content they're looking for, even when it's scattered across multiple locations and content repositories within your organization.

Amazon Kendra is a highly accurate and easy-to-use enterprise search service powered by machine learning (ML). As your users begin to perform searches using Amazon Kendra, you can fine-tune which search results they receive. For example, you might want to prioritize results from certain data sources that are more actively curated and therefore more authoritative. Or if your users frequently search for documents like quarterly reports, you may want to display the more recent quarterly reports first. Relevance tuning allows you to change how Amazon Kendra processes the importance of certain fields or attributes in search results.

Amazon Kendra is a highly accurate and easy-to-use enterprise search service powered by machine learning (ML). As your users begin to perform searches using Kendra, you can fine-tune which search results they receive. For example, you might want to prioritize results from certain data sources that are more actively curated and therefore more authoritative. Or if your users frequently search for documents like quarterly reports, you may want to display the more recent quarterly reports first. Relevance tuning allows you to change how Amazon Kendra processes the importance of certain fields or attributes in search results.

Citibot is a technology company that builds AI-powered chat solutions for local governments such as Fort Worth, Texas; Charleston, South Carolina; and Arlington, Virginia. With Citibot, local residents can quickly get answers to city-related questions, report issues, and receive real-time alerts via text responses. To power these interactions, Citibot uses Amazon Lex, a service for building conversational interfaces for text and voice applications. Citibot built the chatbot to handle basic call queries, which allows government employees to allocate more time to higher-impact community actions.

In today's post, we are going to take a look at another AI service of AWS, Amazon Rekognition. We focus on the image for object and scene detection, and we learn how to use the service programmatically. Furthermore, you can also check out one of my previous posts about another AI Service, Amazon Kendra. Kendra is a service that lets you build your search engine. You can find the code for this post here.

In a bid to enable its enterprise customers gain better insight into business data, Amazon has launched two artificial intelligence services aimed at helping businesses make sense of business data spread across segments and processes. Usage of Machine Learning has been kept on a pedestal while designing these services. The two services - Contact Lens for Amazon Connect and Amazon Kendra are available for its customers using AWS cloud. The services promise to change how enterprise search is done by incorporating new technological capabilities like Natural Language Processing. Amazon Kendra is a plug and play service and does not require expertise in machine learning for the business user.

AWS introduced new services that use AI to allow more developers to apply machine learning to create better end user experiences, including new machine learning-powered enterprise search, code reviews and profiling, fraud detection, medical transcription, and human review of AI predictions. Machine learning continues to grow at a rapid clip, and today there are tens of thousands of customers doing machine learning on AWS (twice as many as the next largest cloud provider), including many customers that opt to use AWS's fully managed AI Services like Alfresco, Bayer Crop Science, Cerner, CJ Cox Automotive, C-SPAN, Deloitte, Domino's, Emirates NBD, Fred Hutchinson Cancer Research Center, FICO, FINRA, Gallup, Kelley Blue Book, Kia, Mainichi Newspapers Co, NASA, PricewaterhouseCoopers, White House Historical Association, and Zola. In the past year, AWS has introduced several new fully managed AI Services like Amazon Personalize and Amazon Forecast that allow customers to benefit from the same personalization and forecasting machine learning technology used by Amazon's consumer business to power its award-winning customer experiences. AWS customers are interested in learning from Amazon's vast experience using machine learning at scale to improve operations and deliver better customer experiences, without needing to train, tune, and deploy their own custom machine learning models. Today, AWS is announcing five new AI services that build upon Amazon's rich experience with machine learning, and allow organizations of all sizes across all industries to adopt machine learning in their enterprises – with no machine learning experience required. Despite many attempts over many years, internal search remains a vexing problem for today's enterprises, and most employees still frequently struggle to find the information they need. Organizations have vast amounts of unstructured text data, much of it incredibly useful if it can be discovered, stored in many formats and spread across different data sources (e.g.