AWS Announces Five New Machine Learning Services That Reinvent and Improve Everyday Enterprise Tasks – With No Machine Learning Experience Required
Amazon Kendra reinvents enterprise search by using natural language processing and other machine learning techniques to unite multiple data silos inside an enterprise and consistently provide high-quality results to common queries instead of a random list of links in response to keyword queries
Amazon CodeGuru helps software developers automate code reviews and identify an application’s most expensive lines of code
Amazon Fraud Detector helps businesses identify online identity and payment fraud in real time, based on the same technology developed for
Amazon Transcribe Medical offers healthcare providers highly accurate, real-time speech-to-text transcription so they can focus on patient care
Amazon Augmented Artificial Intelligence (A2I) helps machine learning developers validate machine learning predictions through human confirmation
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,
Amazon Kendra reinvents enterprise search with machine learning
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. Sharepoint, Intranet, Amazon S3, and on-premises file storage systems). Even with common web-based search tools widely available, organizations still find internal search difficult because none of the available tools do a good job indexing across existing data silos, don’t provide natural language queries, and can’t deliver accurate results. When employees have questions, they are required to use keywords that may appear in multiple documents in different contexts, and these searches typically generate long lists of random links that employees then have to sift through to find the information they seek – if they find it at all.
Amazon Kendra reinvents enterprise search by allowing employees to search across multiple silos of data using real questions (not just keywords) and deploys AI technology behind the scenes to deliver the precise answers they seek (instead of a random list of links). Employees can run their searches using natural language (keywords still work, but most users prefer natural language searches). As an example, an employee can ask a specific question like 'when does the IT help desk open?', and Amazon Kendra will give them a specific answer like ‘the IT help desk opens at 9:30 AM’, along with links back to the IT ticketing portal and other relevant sites. Customers can use Amazon Kendra across their applications, portals, and wikis. With a few clicks in the AWS Management Console, customers point Amazon Kendra at their various document repositories and the service aggregates petabytes of data to build a centralized index. Kendra helps to ensure that search results adhere to existing document access policies by scanning permissions on documents so that search results only contain documents for which the user has permission to access. Additionally, Amazon Kendra actively retrains its machine learning model on a customer specific basis to improve accuracy using click-through data, user location, and feedback to deliver better answers over time. To learn more about Amazon Kendra, visit http://aws.amazon.com/kendra.
Amazon CodeGuru improves software development by using machine learning to provide automated code reviews and helps organizations find their most expensive lines of code
Amazon CodeGuru is a new machine learning service that automates code reviews and finds an application’s most expensive lines of code. There are two components of Amazon CodeGuru – code reviews and application profiling. For code reviews, developers commit their code as usual (support for GitHub and CodeCommit exist today, with more repositories coming over time) and add Amazon CodeGuru as one of the code reviewers, with no other changes to the normal process or software to install. Amazon CodeGuru receives a pull request and automatically starts evaluating the code using pre-trained models that have been trained on several decades of code reviews at
Amazon CodeGuru also contains a machine-learning powered application profiler that helps customers find their most expensive lines of code. To get started, customer install a small, low-profile agent in their application, so Amazon CodeGuru can observe the application run time and profile the application code every five minutes. This code profile includes details on the latency and CPU utilization, linking directly back to specific lines of code. Amazon CodeGuru can help operators find the most expensive line of code in an application, and it produces a flame graph that helps visually identify other lines of code that are creating performance bottlenecks. Amazon’s internal teams have used Amazon CodeGuru to profile code on more than 80,000 applications over the years. From 2017 to 2018, the extensive use of an internal version of Amazon CodeGuru helped the Amazon Prime Day team at Amazon’s consumer business increase its application efficiency, with a 325% increase in CPU utilization, a reduction in the number of instances needed to manage Prime Day, and 39% lower costs overall. To learn more about Amazon CodeGuru, visit http://aws.amazon.com/codeguru.
Amazon Fraud Detector delivers automated fraud detection using machine learning
Tens of billions of dollars are lost to fraud every year by organizations around the world. Today, many AWS customers invest in large, expensive fraud management systems. These systems are often based on hand-coded rules that are time consuming, expensive to customize, and difficult to keep up-to-date as fraud patterns change – all of which results in systems that have lower than desired accuracy. This leads organizations to reject good customers as fraudsters, conduct more costly fraud reviews, and miss opportunities to drive down fraud rates.
Amazon Fraud Detector provides a fully managed service for detecting potential online identity and payment fraud in real time, based on the same technology used by Amazon’s consumer business – with no machine learning experience required. Amazon Fraud Detector uses historical data of both fraudulent and legitimate transactions to build, train, and deploy machine learning models that provide real-time, low-latency fraud risk predictions. To get started, customers upload transaction data to Amazon Simple Storage Service (S3) to customize the model’s training. Customers only need to provide the email address and IP address associated with a transaction, and can optionally add other data (e.g. billing address, or phone number). Based upon the type of fraud customers want to predict (new account or online payment fraud), Amazon Fraud Detector will pre-process the data, select an algorithm, and train a model – using the decades of experience running fraud detection risk analysis at scale at
Amazon Transcribe Medical uses machine learning to transcribe medical speech, allowing health care providers to focus on patient care
Today, physicians are required to conduct detailed data entry into electronic health record (EHR) systems as part of their everyday duties. However, the solutions that help them accurately record and document patient encounters are sub-optimal. At many hospitals, physicians must dictate medical notes into recorders and then submit those voice files to third-party manual transcription services, which is expensive and can take as many as three business days, delaying documentation workflows overall. Another option is to leverage existing front-end dictation software, but existing tools are limited and physicians still end up spending several hours on clinical documentation every day. A third option is for healthcare providers to hire human scribes to assist physicians with notetaking during patient encounters, but human scribes can be unsettling to patients, physicians often mention that their output is imperfect, and medical organizations struggle to schedule and coordinate scribes at scale. Existing solutions fall short, both in terms of enhancing clinical documentation efficiency and enabling better patient care.
Amazon Transcribe Medical solves these problems by using machine learning technology to automatically transcribe natural medical speech. Clinical documentation applications built on top of Amazon Transcribe Medical’s speech-to-text capabilities produce accurate and affordable transcripts. Amazon Transcribe Medical consists of multiple machine learning models that have been trained on tens of thousands of hours of medical speech to deliver accurate, machine learning-powered medical transcription. Transcripts are generated in real time, eliminating the multi-day turnarounds. Amazon Transcribe Medical can help physicians automatically transcribe conversations during the patient encounter without the distraction of manual notetaking, allowing health care providers to focus on patient care. Physicians can speak naturally, and Amazon Transcribe Medical uses built-in automatic punctuation to overcome the limitations of existing transcription software. For healthcare providers, voice solutions built on Amazon Transcribe Medical are scalable to thousands of potential medical centers, removing the operational pain of managing and coordinating temporary scribes. Amazon Transcribe Medical is HIPAA eligible, and offers an easy-to-use API that can integrate with voice-enabled applications and any device with a microphone. Text output from Amazon Transcribe Medical can also be used by other AWS services, such as Amazon Comprehend Medical, a natural language processing service, for downstream data analysis before final entry into EHR systems. To get started with Amazon Transcribe Medical, visit http://aws.amazon.com/transcribe/medical.
Amazon Augmented Artificial Intelligence (A2I) allows developers to validate machine learning predictions with human reviewers
Machine learning can provide highly accurate predictions for a variety of use cases, including identifying objects in images, extracting text from scanned documents, or transcribing and understanding spoken language. In each case, machine learning models provide a prediction and also a confidence score that expresses how certain the model is in its prediction. The higher the confidence number, the more the result can be trusted. For many use cases, when developers receive a high confidence result, they can trust that the results are likely to be accurate, and they can process them automatically (e.g. automatically moderating user-generated content on a social network, or adding subtitles to a video). However, in situations where confidence is lower than desired, due to some ambiguity in the prediction result, results may require a human review to resolve this ambiguity. This interplay between machine learning and human reviewer is critical to the success of machine learning systems, but human reviews are challenging and expensive to build and operate at scale, often involving multiple workflow steps, custom software to manage human review tasks and results, and recruiting and managing large groups of reviewers. As a result, developers sometimes spend more time managing the human review process than building their intended application, or they have to forego having a human review, which leads to less confidence and utility in many predictions.
Amazon Augmented Artificial Intelligence (A2I) is a new service that makes it easier to build and manage human reviews for machine learning applications. Amazon A2I provides pre-built human review workflows for common machine learning tasks (e.g. object detection in images, transcription of speech, and content moderation) that allow machine learning predictions from Amazon Rekognition and Amazon Textract to be human-reviewed more easily. Developers choose a confidence threshold for their specific application and all predictions with a confidence score below the threshold are automatically sent to human reviewers for validation. Developers can choose to have their reviews performed by Amazon Mechanical Turk’s 500,000 global workers, third-party organizations with pre-authorized workers (including
“Companies across various industry segments tell us that they want to leverage Amazon’s extensive experience with machine learning to address some of the common challenges they face as enterprises on an on-going basis. These challenges include internal search, helping software developers write better code, identifying fraudulent transactions, and improving the overall quality of all machine learning systems,” said Swami Sivasubramanian, Vice President, Amazon Machine Learning, AWS. “With decades of experience in building machine learning systems,
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