How some of AWS's most innovative customers are using computer vision technologies

From counting fish to identifying touchdowns, AWS customers are utilizing computer vision and pattern recognition technologies to improve business processes and customer experiences.

Computer vision, the automatic recognition and description of images and video, has applications that are far-reaching, from identifying defects in high speed assembly lines and its use in autonomous robots, to the analysis of medical images, and the identification of products and people in social media. This week, in line with the IEEE Computer Vision and Pattern Recognition (CVPR) conference, we’ve rounded up examples of how some of AWS's most innovative customers are utilizing computer vision and pattern recognition technologies to improve business processes and customer experiences. This includes approaches such as data scientists building custom vision models using Amazon SageMaker, and application developers using Amazon Rekognition and Amazon Textract to embed computer vision into their applications.

Advertising

REA Group image
REA Group has developed an image compliance system that automatically detects any noncompliance and notifies home sellers.
fstop123/Getty Images

In advertising and other online media, computer vision can automate content moderation. REA Group, a multinational digital advertising company specializing in property and real estate, provides search-based portals that enable property sellers to upload images of properties on the market to deliver a wide, searchable selection to their consumers. REA Group discovered that images uploaded to their portal often weren’t compliant with their usage terms. Some images included trademarks or contact details of the sellers, which created lead attribution challenges. They set up a dedicated team of individuals to manually review the images for unapproved content, but the large volume of daily uploads and the additional review process delayed the property listing time by several days. The REA team developed an image compliance system that automatically detects any noncompliance and notifies sellers. To augment their existing machine learning models, they're using Amazon Rekognition Text in Image, which detects and extracts text in images, enabling them to increase the accuracy of detecting noncompliance and reduce false positives by more than 56 percent. They added business rules that factored in a variety of predictions from their own models, and from Amazon Rekognition, to enable automated decision-making.

Agriculture

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Aquabyte's machine learning algorithms can estimate how much a fish weighs while still in the water.

Agriculture has also benefited from computer vision. Fish farming is one of the most efficient sources of protein, since a pound of feed equates to nearly a pound of protein. But the cold, dark waters of fish habitats make it nearly impossible to effectively manage these farms from the surface. Historically, fish farmers have had to randomly scoop fish out of the water to measure their weight and check for disease. Aquabyte’s machine learning solution reimagines this process by using underwater cameras that keep tabs on the fish and compare photos of them over time. The machine learning algorithms, running on Amazon SageMaker, can estimate how much each fish weighs while it’s still in the water. The system can also monitor the fish for sea lice, a parasite that is a major problem in salmon farms, and the subject of significant regulation in Norway, where the bulk of Aquabyte’s client base currently operates. Without a solution like Aquabyte, managing sea lice amounts to nearly a quarter of the cost of operating a salmon farm. Aquabyte’s cameras have counted 2 million sea lice to date, the result of billions of images being captured. The Aquabyte team has been working on methods that would allow farmers to track individual fish for growth-tracking and breeding purposes. In the future, machine learning might even help automate elements of the farms by intelligently distributing fish feed, for example.

Autonomous driving

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DeepMap is focused on solving the mapping and localization challenge for autonomous vehicles.

Industries like autonomous driving wouldn’t even be possible without the help of computer vision. Perhaps you think the world is already sufficiently mapped. With the advent of satellite images and Google Street View, it seems like every square inch of the globe is represented in data. But for autonomous vehicles, much of the world is uncharted territory. That’s because the maps designed for humans “can’t be consumed by robots,” says Tom Wang, the director of engineering at DeepMap, a Palo Alto startup focused on solving the mapping and localization challenge for autonomous vehicles. According to Wang, these new kinds of vehicles need higher precision maps with richer semantics, things like the traffic signals, a lot of different traffic signs, driving boundaries, and connecting lanes. For DeepMap computer vision is critical. DeepMap needs to run a vast volume of image detections to automatically generate a comprehensive list of map features and detect dynamic road changes. Using Amazon SageMaker, DeepMap updates training models within a day and runs image detection on tens of millions of images on a daily basis to keep up with ever-changing conditions.

Education

Certipass, a UNI ISO standards accredited body for the certification of digital skills
Certipass was able to build their solution in under 30 days, enabling all their testing centers to test candidates online during the COVID-19 pandemic.
fizkes/Getty Images/iStockphoto

In the wake of the COVID-19 pandemic, many educational institutions needed to quickly pivot to the online proctoring of exams, leading to a need for new ways to verify identification. Certipass, a UNI ISO standards accredited body for the certification of digital skills, is the primary provider of the international digital competency certification –European Informatics Passport (EIPASS).

Since the EIPASS Certification is an international standard, Certipass has made it their mission to ensure maximum security, objectiveness, transparency, and fairness during the entire online evaluation process. Certipass used Amazon Rekognition for automated candidate identity verification during tests that are in line with e-Competence Framework for Information and Communication Technology (CEN) and The Digital Competence Framework for Citizens (Joint Research Centre). They were able to build the solution in under 30 days to enable all their testing centers to test candidates online during COVID-19.

Financial services

Aella Credit
Aella Credit provides easy access to credit in emerging markets using biometric, employer, and mobile phone data
Victor Karanja/Getty Images

In financial services, Aella Credit provides easy access to credit in emerging markets using biometric, employer, and mobile phone data. For those in emerging markets, identity verification and validation is one of the major challenges to accessing retail banking services. How can you know that people are who they say they are in communities that don't have proper identification systems? Aella Credit uses Amazon Rekognition to analyze images to verify a customer’s identity and give them access to financial and healthcare services with minimal friction. Amazon Rekognition helps to automate video and image analysis, with no machine learning expertise required. What would have taken days to verify someone’s identity manually, now happens in seconds. Customers can actually receive their loan in their account in less than five minutes, broadening access to credit.

Financial technology

To make sure users are getting the largest possible tax refund, Intuit incorporates machine learning throughout the TurboTax experience to help users file their taxes more efficiently. TurboTax uses machine learning to shorten the filing process, which takes an average of 13 hours.

Taxes image for AWS customer success story
TurboTax utilizes machine learning to shorten the filing process.
simpson33/Getty Images/iStockphoto

With Intuit’s computer vision capabilities supported by Amazon Textract, entering information from tax forms like W2s or 1099s takes seconds. Rather than a user having to enter form fields manually, the service scans pictures of the forms and digitizes them. Then, using contextual data from TurboTax’s existing database of tax codes and compliance forms, Amazon Textract verifies accuracy and identifies any anomalies or missing data for the user.

Healthcare

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By combining the power of machine learning and computer vision, an interdisciplinary team of researchers at Duke University has created a faster, less expensive, more reliable, and more accessible system to screen children for autism spectrum disorder.

Machine learning plays a key role in many health-related realms - from providers and payers looking to expedite the care continuum to pharma and biotech researchers looking to reduce costs and speed up the drug discovery and disease detection process. Researchers at Duke Center for Autism and Brain Development are using machine learning to screen for autism spectrum disorder (ASD) in children. It’s critically important to diagnose ASD as early in a child’s development as possible — starting treatment for ASD at an age of 18 to 24 months can increase a child’s IQ by up to 17 points—in some cases moving them into the “average” child IQ range of 90-110 (or above it)—and, in turn, significantly improving their quality of life. Currently, the wait time for children to receive a diagnosis could be well after the child’s third birthday. By combining the power of machine learning and computer vision, powered by AWS, an interdisciplinary team of researchers at Duke University have created a faster, less expensive, more reliable, and more accessible system to screen children for ASD.

Media and entertainment

Computer vision technology is helping sports organizations like the National Football League (NFL) improve the game for fans. The NFL works with AWS to develop real-time, state-of-the-art cloud technology leveraging machine learning and artificial intelligence to increase the efficiency and pace of the game.

For example, deep learning and computer vision technologies are being explored to aid game officiating including real-time football tracking. Within days, AWS and NFL scientists were able to create custom training data sets of thousands of images extracted from NFL broadcast game footage using Amazon SageMaker Ground Truth.

NFL football
Deep learning and computer vision technologies are being explored by the NFL to aid game officiating, including real-time football tracking.
CREDIT: National Foottball League

Working with the Amazon ML Solutions Lab, Amazon SageMaker and GluonCV with MXNet were used to train and optimize several state-of-the-art deep learning-based object detection models such as Faster-RCNN and Yolov3, to accurately detect the football across video frames. This led to a first-of-its-kind football tracking model that performs well in a number of complex scenarios, such as when the ball is highly occluded or is partially visible in different camera angles.

The NFL also uses computer vision to more easily and quickly search through thousands of media assets. The NFL photo team, official photographers of the NFL, has millions of photos in archive and generates 500,000 photos each season. Manually, they were able to tag 50,000 images over 18 months. By using Amazon Rekognition custom face collection, text in image, object detection, and Custom Labels, an automated machine learning object detection service, they were able to apply detailed tags for players, teams, objects, action, jerseys, location, etc. to their entire photo collection in a fraction of time it took previously. This allowed them to make these photos searchable and usable to everyone in the company in ways that weren't possible before.

For Sportradar, the global provider of sports and intelligence for the betting and media industries providing data coverage from more than 200,000 events annually, advances in computer vision are an opportunity to expand the depth of sports data offered to customers and reduce the costs of data collection through automation.

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For Sportradar, advances in computer vision are an opportunity to expand the depth of sports data offered to customers and reduce the costs of data collection through automation.
scyther5/Getty Images/iStockphoto

Sportradar is investing in computer vision research both through internal development and external partnerships to build computer vision data collection capabilities with an initial focus on tennis, soccer and snooker. Working with the Amazon ML Solutions Lab, Sportradar is exploring the application of state-of-the-art deep learning models for automated match event detection in soccer, moving beyond player and ball localization to understanding the intent of the play in terms of what is happening in the game.

To bring this technology into production as it matures, Sportradar is leveraging AWS services including Amazon SageMaker, EKS, MSK, FSx and Amazon’s broad range of GPU and CPU compute instances for its computer vision processing pipeline. This infrastructure allows Sportradar's researchers to test and validate computer vision models at scale and bring models from the lab to production with minimal effort while delivering the low latency, reliability and scalability needed for live sports betting use cases.

You can find more ways that AWS customers are innovating with computer vision here. More information about Amazon's participation at CVPR is available here.

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GB, MLN, Edinburgh
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IN, TS, Hyderabad
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US, WA, Seattle
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US, CA, Palo Alto
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US, WA, Seattle
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US, WA, Bellevue
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US, WA, Seattle
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FR, Paris
Are you a MS or PhD student interested in a 2025 Internship in the field of machine learning, deep learning, speech, robotics, computer vision, optimization, quantum computing, automated reasoning, or formal methods? If so, we want to hear from you! We are looking for students interested in using a variety of domain expertise to invent, design and implement state-of-the-art solutions for never-before-solved problems. You can find more information about the Amazon Science community as well as our interview process via the links below; https://www.amazon.science/ https://amazon.jobs/content/en/career-programs/university/science https://amazon.jobs/content/en/how-we-hire/university-roles/applied-science Key job responsibilities As an Applied Science Intern, you will own the design and development of end-to-end systems. You’ll have the opportunity to write technical white papers, create roadmaps and drive production level projects that will support Amazon Science. You will work closely with Amazon scientists, and other science interns to develop solutions and deploy them into production. You will have the opportunity to design new algorithms, models, or other technical solutions whilst experiencing Amazon’s customer focused culture. The ideal intern must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. A day in the life At Amazon, you will grow into the high impact, visionary person you know you’re ready to be. Every day will be filled with developing new skills and achieving personal growth. How often can you say that your work changes the world? At Amazon, you’ll say it often. Join us and define tomorrow. Some more benefits of an Amazon Science internship include; • All of our internships offer a competitive stipend/salary • Interns are paired with an experienced manager and mentor(s) • Interns receive invitations to different events such as intern program initiatives or site events • Interns can build their professional and personal network with other Amazon Scientists • Interns can potentially publish work at top tier conferences each year About the team Applicants will be reviewed on a rolling basis and are assigned to teams aligned with their research interests and experience prior to interviews. Start dates are available throughout the year and durations can vary in length from 3-6 months for full time internships. This role may available across multiple locations in the EMEA region (Austria, Estonia, France, Germany, Ireland, Israel, Italy, Luxembourg, Netherlands, Poland, Romania, Spain, UAE, and UK). Please note these are not remote internships.
GB, London
Are you looking to work at the forefront of Machine Learning and AI? Would you be excited to apply cutting edge Generative AI algorithms to solve real world problems with significant impact? The AWS Industries Team at AWS helps AWS customers implement Generative AI solutions and realize transformational business opportunities for AWS customers in the most strategic industry verticals. This is a team of data scientists, engineers, and architects working step-by-step with customers to build bespoke solutions that harness the power of generative AI. The team helps customers imagine and scope the use cases that will create the greatest value for their businesses, select and train and fine tune the right models, define paths to navigate technical or business challenges, develop proof-of-concepts, and build applications to launch these solutions at scale. The AWS Industries team provides guidance and implements best practices for applying generative AI responsibly and cost efficiently. You will work directly with customers and innovate in a fast-paced organization that contributes to game-changing projects and technologies. You will design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience. In this Data Scientist role you will be capable of using GenAI and other techniques to design, evangelize, and implement and scale cutting-edge solutions for never-before-solved problems. Key job responsibilities As an Data Scientist, you will- - Collaborate with AI/ML scientists, engineers, and architects to research, design, develop, and evaluate cutting-edge generative AI algorithms and build ML systems to address real-world challenges - Interact with customers directly to understand the business problem, help and aid them in implementation of generative AI solutions, deliver briefing and deep dive sessions to customers and guide customer on adoption patterns and paths to production - Create and deliver best practice recommendations, tutorials, blog posts, publications, sample code, and presentations adapted to technical, business, and executive stakeholder - Provide customer and market feedback to Product and Engineering teams to help define product direction About the team Diverse Experiences Amazon values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why AWS Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. Mentorship and Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. AWS Sales, Marketing, and Global Services (SMGS) is responsible for driving revenue, adoption, and growth from the largest and fastest growing small- and mid-market accounts to enterprise-level customers including public sector. The AWS Global Support team interacts with leading companies and believes that world-class support is critical to customer success. AWS Support also partners with a global list of customers that are building mission-critical applications on top of AWS services. AWS Professional Services engage in a wide variety of projects for customers and partners, providing collective experience from across the AWS customer base. We are obsessed about delivering success for the Customer. Our team collaborates across the entire AWS organization to bring access to product and service teams, to get the right solution delivered and drive feature innovation based upon customer needs.
US, NY, New York
Amazon's Global XCM organization (Cross-Category, Cross-Channel Marketing) is looking for a talented Economist who is interested in solving one of the most challenging business problems in marketing measurement and optimization, and developing ML model. XCM’s mission is to be the most culturally and contextually aware, creatively breakthrough, and measurably effective marketing organization in the world. In this role, you will pioneer real-time marketing measurement and optimization solutions by combining causal inference with advanced ML models. You'll leverage Amazon's customer behavior data and external datasets to build scalable solutions, while partnering with marketing stakeholders and product teams to translate business needs into actionable insights. Your expertise in causal inference and machine learning will be crucial in enhancing customer engagement and marketing effectiveness across Amazon's global marketing ecosystem. Key job responsibilities - Design and implement advanced causal inference and machine learning solutions for marketing measurement and optimization; - Transform complex business requirements into technical questions - Translate model outputs to actionable insights through close collaboration with marketing stakeholders and product teams; - Work with product and engineering teams to deploy the model into automation system