AWS A2I Augments AI ML Capabilities Applied to Complex Scenarios – AiThority

  • Lauren
  • April 28, 2020
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AWS A21 allows you to implement a human review of ML predictions and work with your choice of  AI ML applications.Amazon Web Services (AWS) has announced the general availability of the Amazon Augmented Artificial Intelligence (A2I) to make it seamlessly easier for DevOps teams to validate their Machine Learning predictions. A2I will enable developers to build and manage complex workflows with the interjection of human reviews and recommendations. The new AI ML platform is already put to good use among AWS customers, including National Health Service (NHS UK), T-Mobile, and Deloitte.What is AWS A2I?Amazon Augmented Artificial Intelligence (A2I) is a fully managed service that makes it easy to add a Human review to Machine Learning predictions. A2I improves model and application accuracy by continuously identifying and improving low confidence predictions.In addition, Amazon A2I also helps developers add a human review for model predictions to new or existing applications using reviewers from Mechanical Turk, third-party vendors, or their own employees.Amazon Web Services already makes the broadest and deepest set of AI ML algorithms and models to solve some of the toughest challenges faced by businesses. AWS A2I, in totality, brings the essence of AI ML deployment to every developer’s dashboard, powering quick adoption and further refining of pre-trained AI tools and techniques for Computer Vision, Natural Language Processing, Neural Networking, Cognitive Learning, Deep Learning, and Predictive Intelligence.How AWS A2I Works?Validate AIOps to Build Human Review SystemsModern Machine Learning tools and platforms offer Inferences in real-time for a wide array of business scenarios and use-cases. These are extracted predictions from testing and validating data from Object Detection, Image Recognition, Computer Vision, Big Data Analytics, and NLP. In each case, Machine Learning models are required to provide accurate predictions/inferences. Based on a specific rating model, these AI ML tools are ranked for their accuracy, efficacy, and traceability.AWS calls it “the Confidence Number.” The higher the confidence number, the greater is its trust factor. Once a large sample result can be trusted, MLOps teams can fully automate a process using a high-rated Confidence Number Machine Learning model. In some cases, human reviewers are also expected to provide their inferences. It is highly recommended that confidence numbers are high for both Automated ML and Human reviewer systems to achieve a successful AI ML interplay.Analyzing Human Interactions and their reviews can prove to be a costly, inaccurate, and time-consuming task. Hence, the AWS A2I application makes its way in.Amazon A2I makes it easier for developers to build the human review system, structure the review process, and manage the human review workforce. AI ML developers could use Amazon A2I to quickly spin up and manage a workforce of humans to review and validate the accuracy of Machine Learning predictions for an application that extracts financial information from scanned mortgage documents or an application that uses image recognition to identify counterfeit items online so that the quality of results improve over time. There are no upfront commitments to use Amazon A2I, and users pay only for each review needed.Get Every Review Pre-screened by AWS for Quality and Security ProceduresWith Amazon A2I, developers can add a human review to machine learning applications without the need to build or manage expensive and cumbersome systems for human review.Amazon A2I provides over 60 pre-built human review workflows for common Machine Learning tasks (e.g. object detection in images, transcription of speech, and content moderation, etc.) that allow Machine Learning predictions from Amazon Rekognition and Amazon Textract to be human-reviewed more easily.A2I Provides Custom ML Model Built With Amazon SageMaker or Other SolutionsDevelopers who build custom Machine Learning models in Amazon SageMaker (or other on-premises or cloud tools) can set up a human review for their specific use case in the Augmented AI console or via its Application Programming Interface (API).After setting a confidence threshold for model predictions, developers can choose to have predictions below that threshold reviewed by Amazon Mechanical Turk and its 500,000 global workforce of independent contractors, third-party organizations who specialize in business process outsourcing (e.g. iVision, CapeStart Inc., and iMerit), or their own private, in-house reviewers.At the time of this announcement, Swami Sivasubramanian, Vice President, Amazon Machine Learning, Amazon Web Services, Inc. said —“We often hear from our customers that Amazon SageMaker helps speed training, tuning, and deploying custom machine learning models, while fully managed services like Amazon Rekognition and Amazon Textract make it easy to build applications that incorporate machine learning without requiring any machine learning expertise. But even with these advancements, our customers still say there are critical use cases where human judgment is required like in law enforcement investigations or times when the human review can be used to resolve the ambiguity in predictions when confidence levels fall below a given threshold for less sensitive use cases, and the current human review process involves a lot of custom effort and cost.”Developers can specify the number of workers per review, and Amazon A2I then routes each review to the precise number of reviewers.For example, a company building a system for processing financial loan applications using Amazon Textract can easily configure Amazon A2I to work with Amazon Textract outputs such that forms that have a confidence score less than 99% will be routed to human reviewers from their private workforce. Human-validated results are stored in Amazon Simple Storage Service (S3), and developers can set up Amazon CloudWatch Events notifications to review metadata about inference accuracy and retrieve the results.Swami added, “Today, we’re excited to help our customers remove another obstacle to building Machine Learning applications with the launch of Amazon A2I, which makes it significantly easier and faster to incorporate human judgment into Machine Learning applications in order to ensure higher quality predictions over a sustained period of time.”General Availability and Customer CommentsAmazon A2I is available today in US East (N. Virginia), US East (Ohio), US West (Oregon), Canada (Central), EU West (London), EU West (Ireland), EU (Frankfurt), Asia Pacific (Singapore), Asia Pacific (Tokyo), Asia Pacific (Sydney), Asia Pacific (Seoul), and Asia Pacific (Mumbai).Amazon Textract is CompellingChris Suter, Head of Cloud Platforms and Innovation, NHS BSA said –“The NHS is investing in the promise of AI to improve the quality of public healthcare across the UK. Human judgment is critical and in fact, is often required for decisions involving medical payments.”Chris added, “Amazon Textract is compelling because it offers AI-powered extraction of text and structured data from virtually any document. We are excited about Amazon Augmented AI because it allows us to take advantage of Machine Learning while still applying human judgment. That’s a game-changer for us.”National Health Service, Business Services Authority (NHS BSA) is part of the UK National Health Service and provides a range of support services to NHS organizations, NHS contractors, and patients. As part of their business process services, they process 54 million paper prescriptions and other healthcare documents each month.Deliver Top-Quality Insights by Having Humans Validate Random Samples of Model PredictionsAs America’s Un-carrier, T-Mobile US, Inc. is redefining the way consumers and businesses buy wireless services through leading product and service innovation.Heather Nollis, Machine Learning Engineer, T-Mobile said –“Providing relevant information, such as account details and available discounts, in real time to our customer care agents while they are in live conversations with customers is one of the ways T-Mobile uses machine learning to improve customer experience. Using A2I, we will be able to ensure that our models continuously deliver top-quality insights by having humans validate random samples of model predictions.”Heather added, “Trust is the hardest thing to build when it comes to machine learning, and A2I will allow us to make sure that our models are making the fewest mistakes.”Use A2I to Help Verify the Accuracy of ML Models for Automated Image-Based ApplicationsBeena Ammanath, Managing Director at Deloitte Consulting LLP said –“Part of setting our clients up for success is helping them leverage the latest technology. Using machine learning enables us to help improve our clients’ systems and boost their productivity while reducing time to market for products, services, and applications. As part of providing the latest advancements in ML to our clients, we see the benefits of human-in-the-loop systems adding an extra layer of confidence to ML applications.”Beena added, “Our clients in the insurance industry, for example, could use A2I to help verify the accuracy of ML models for automated image-based vehicle damage detection and analysis of text-based insurance claims. We’re excited to see the many ways our clients across industries could benefit from incorporating A2I into their ML A2I workflows.”A2I Trains and Improves AI ML Models Over Time Through Continuous Auditing and ImprovementBelle Fleur believes the machine learning revolution is altering the way we live, work, and relate to one another, and will transform every business in every industry. “We started using Amazon Textract with one of our financial services clients and quickly realized that coupling that service with Amazon A2I allows them to go through huge quantities of documents and extract relevant data needed for their clients. Adding Amazon A2I helped us incorporate human judgment for documents that require contextual interpretation and validate the data,” said Tia Dubuisson, President at Belle Fleur.Tia added, “Not only did this decrease the time spent on human validation, but it also pulled all the relevant extracted data into one place in an easy to understand the workflow for reviewers so that they are able to quickly and easily review machine learning outputs from Amazon Textract. Amazon A2I not only provides us and our customers’ peace of mind that the more nuanced data extracted are reviewed by humans, but it also helps train and improves our machine learning models over time through continuous auditing and improvement.”We are yet to test how A2I would integrate with Amazon SageMaker, and the validation of the pair’s role in enabling AIOps teams to build, train and deploy machine learning models at scale, including from open-source frameworks.(To share your AI ML methodology, please write to us at [email protected])Share and Enjoy !