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.

Sports betting image for AWS customer success story
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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US, WA, Seattle
Do you want to join Alexa AI -- the science team behind Amazon’s intelligence voice assistance system? Do you want to utilize cutting-edge deep-learning and machine learning algorithms to delight millions of Alexa users around the world?If your answers to these questions are “yes”, then come join us at the Alexa Artificial Intelligence team, which is in charge of improving Alexa user satisfaction through real-time metrics monitoring and continuous closed-loop learning. The team owns the modules that reduce user perceived defects and frictions through utterance reformulation, contextual and personalized hypothesis ranking.With the Alexa Artificial Intelligence team, you will be working alongside a team of experienced machine/deep learning scientists and engineers to create data driven machine learning models and solutions on tasks such as sequence-to-sequence query reformulation, graph feature embedding, personalized ranking, etc..You will be expected to:· Analyze, understand, and model user-behavior and the user-experience based on large scale data, to detect key factors causing satisfaction and dissatisfaction (SAT/DSAT).· Build and measure novel online & offline metrics for personal digital assistants and user scenarios, on diverse devices and endpoints· Create and innovate deep learning and/or machine learning based algorithms for utterance reformulation and contextual hypothesis ranking to reduce user dissatisfaction in various scenarios;· Perform model/data analysis and monitor user-experienced based metrics through online A/B testing;· Research and implement novel machine learning and deep learning algorithms and models.
US, CA, San Francisco
Can Alexa help anyone experience the music they enjoy? Even if they don't know what they'd like to listen to in this moment? Or, if they know they want “Happy rock from the 90s”, can she help them find it?Your machine learning skills can help make that a reality on the Amazon Music team. We are seeking an Applied Scientist who will join a team of experts in the field of machine learning, and work together to break new ground in the world of understanding and classifying different forms of music, and creating interactive experiences to help users find the music they are in the mood for. We work on machine learning problems for music classification, recommender systems, dialogue systems, NLP, and music information retrieval.You'll work in a collaborative environment where you can pursue ambitious, long-term research, with many peta-bytes of data, work on problems that haven’t been solved before, quickly implement and deploy your algorithmic ideas at scale, understand whether they succeed via statistically relevant experiments across millions of customers, and publish your research. You'll see the work you do directly improve the experience of Amazon Music customers on Alexa/Echo, mobile, and web.The successful candidate will have a PhD in Computer Science with a strong focus on machine learning, or a related field, and 2+ years of practical experience applying ML to solve complex problems in recommender systems, information retrieval, signal processing, NLP or dialogue systems. Great if you have a passion for music, but this is not a requirement.Responsibilities:- Advance long-term, exploratory research projects in machine learning and related fields to create highly innovative customer experiences;- Analyze large amounts of Amazon’s customer data to discover patterns, find opportunities, and develop highly innovative, scalable algorithms to seize these opportunities;- Validate new or improved models via statistically relevant experiments across millions of customers;- Work closely with software engineering teams to build scalable prototypes for testing, and integrate successful models and algorithms in production systems at very large scale.Amazon MusicImagine being a part of an agile team where your ideas have the potential to reach millions. Picture working on cutting-edge consumer-facing products, where every single team member is a critical voice in the decision-making process. Envision being able to leverage the resources of a Fortune-500 company within the atmosphere of a start-up. Welcome to Amazon Music, where ideas are born and come to life as Amazon Music Unlimited, Prime Music, and so much more.Everyone on our team has a meaningful impact on product features, new directions in music streaming, and customer engagement. We are looking for new team members across a variety of job functions including software engineering/development, marketing, design, ops and more. Come join us as we make history by launching exciting new projects in the coming year.Our team is focused on building a personalized, curated, and seamless music experience. We want to help our customers discover up-and-coming artists, while also having access to their favorite established musicians. We build systems that are distributed on a large scale, spanning our music apps, web player, and voice-forward audio engagement on mobile and Amazon Echo devices, powered by Alexa to support our customer base. Amazon Music offerings are available in countries around the world, and our applications support our mission of delivering music to customers in new and exciting ways that enhance their day-to-day lives.Come innovate with the Amazon Music team!
US, WA, Seattle
Organizational Research and Measurement is seeking a multi-disciplinary data scientist to enable and advance our Diversity, Equity and Inclusion work. As Amazon World Wide Consumer increases the speed and complexity of our operations, we need to find new ways to help employees grow and succeed. As a data scientist on the team, you will have the opportunity to work on one of the world’s largest employee data sets and influence the long-term evolution of how we understand and support employees as professionals and individuals.The ideal candidate for this role will be a collaborative team player with strong technical abilities, communication skills, project management experience, and a proactive, start-up-style mentality. You possess solid analytical skills in data science and excels in deriving actionable business insights that drive positive changes. You will have hands-on experience leading product development initiatives, and are able to balance technical leadership with strong business judgment to make the right decisions about technology, models, and methodologies choices. You strive for simplicity, learn quickly, and demonstrate significant creativity and high judgment backed by data.Responsibilities· Enable team data efficacy and efficiency· Lead the data aspects of scientific research projects from start to finish, including experiment / study execution, data gathering and manipulation, synthesis and modeling, reporting, and solution implementation· Design, Plan, build, and maintain experimental and production systems that take inputs from multiple models and data sources to support scalable, iterative, and continuous experimentation· Work with Amazon Operations and HR to identify opportunities for enhancing and enriching data sources to the team· Collaborate with customers, stakeholders, engineers, scientists, and program and product owners· Identify, assess, track, and mitigate data related issues and risks at multiple levels· Seek out and capitalize on opportunities to innovate new data technologies as needs· Identify, apply, and advance best practices across the disciplines of statistics, applied psychology, software development, and data science· Own the advancement of the team’s data analytical capabilities· Identify and pursue collaboration opportunities with other data teams working on parallel or complimentary projects· Manage relationships with other data teams necessary for our team to grow and function· Provide technical leadership in data science and mentoring to team members
US, WA, Seattle
The Organizational Research and Measurement team conducts research supporting all Amazon corporate employees. Our goal is to build talent systems to enable employees to thrive at Amazon. We focus on the entire employee life-cycle to improve both business and employee outcomes. This entails doing longitudinal survey research, organizational network analysis, experimental and quasi-experimental studies for causal inference, building services that plug-in to other tools, and providing general consultation to stakeholders. We aim to improve the outcomes of our business and employees by doing cutting edge social science research.The Role:As a Data Scientist at Amazon, your main focus will be on developing predictive models, simulation, visualization, and support structures for data analysis.
US, WA, Seattle
The Organizational Research and Measurement team conducts research supporting all Amazon corporate employees. Our goal is to build talent systems to enable employees to thrive at Amazon. We focus on the entire employee life-cycle to improve both business and employee outcomes. This entails doing longitudinal survey research, organizational network analysis, experimental and quasi-experimental studies for causal inference, building services that plug-in to other tools, and providing general consultation to stakeholders. We aim to improve the outcomes of our business and employees by doing cutting edge social science research.The Role:As a Data Scientist at Amazon, your main focus will be on developing predictive models, simulation, visualization, and support structures for data analysis.
US, WA, Seattle
The Organizational Research and Measurement team conducts research supporting all Amazon corporate employees. Our goal is to build talent systems to enable employees to thrive at Amazon. We focus on the entire employee life-cycle to improve both business and employee outcomes. This entails doing longitudinal survey research, organizational network analysis, experimental and quasi-experimental studies for causal inference, building services that plug-in to other tools, and providing general consultation to stakeholders. We aim to improve the outcomes of our business and employees by doing cutting edge social science research.The Role:As a Research Scientist at Amazon, you will apply scientific principles, subject matter expertise, and business acumen to deliver results at scale by conducting employee life-cycle research.Key Responsibilities:· Supporting global-scale research initiatives across multiple business segments and implementing a wide range of scientific methodologies to solve stakeholder problems.· Collaborating with a cross-functional team that has expertise in the social sciences (e.g., econometrics, psychometrics, judgement and decision making models), machine learning, data science, data engineering, and business intelligence· Querying from multiple data sources, data cleaning and exploration, and advanced statistical analysis.· Writing high-quality, evidence-based documents that help provide insights to business leaders and gain buy-in.· Convert evidence-based insights into usable products, services, or tools for stakeholders.· Serving as a subject matter expert on a wide variety of topics related to research design, measurement, and analysis.· Sharing knowledge, advocating for innovative solutions, and supporting team members.
US, WA, Seattle
The AWS Central Economics team is looking for a PhD economist. The ideal candidate will have experience with time-series forecasting.You will learn about cloud products, including compute, storage, and databases. You will work on analytic projects requested by senior leadership. You will get the opportunity to learn new techniques. You will be a part of a team with many experienced economists.Amazon is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation
US, WA, Seattle
The Organizational Research and Measurement (ORM) Team within World Wide Consumer guides the talent strategy for Amazon’s largest workforce comprised of over one million Amazon global employees across order fulfillment, transportation, corporate, consumer, and customer service organizations. We are an interdisciplinary team that combines the talents of science and engineering to develop and deliver data products and solutions across the employee lifecycle including onboarding; high-potential identification; talent development; engagement; movement; retention; and attrition.We are looking for an applied scientist who is able to engineer end-to-end solutions for complex science and business problems around the future of talent evaluation, development, and management at Amazon. You will work closely with I/O psychologists, economists, engineers, and business partners to estimate and validate models on large scale data, and turn the results of these analyses into products, policies, programs, and actions that have a major impact on Amazon’s business. We are looking for creative thinkers who can combine strong data science, software engineering, UX and product development skillsets with a desire to innovate, and who know how to execute and deliver on big ideas.Key Responsibilities· Develop of start-to-finish data product solutions from requirements gathering and ideation, through interface design and implementation.· Design data infrastructure and pipelines for machine learning and analytics products.· Obtain, merge, analyze, and report data using SQL, statistics software, and data visualization tools.· Apply various statistical and machine learning techniques to analyze large and complex data sets.· Communicate applied machine learning and statistic concepts to project sponsors, business leaders, and development teams across Amazon.· Understand business customer needs, iterate on feedback, and drive adoption
US, WA, Seattle
The Organizational Research and Measurement (ORM) Team within World Wide Consumer guides the talent strategy for Amazon’s largest workforce comprised of over one million Amazon global employees across order fulfillment, transportation, corporate, consumer, and customer service organizations. We are an interdisciplinary team that combines the talents of science and engineering to develop and deliver data products and solutions across the employee lifecycle including onboarding; high-potential identification; talent development; engagement; movement; retention; and attrition.We are looking for an applied scientist who is able to engineer end-to-end solutions for complex science and business problems around the future of talent evaluation, development, and management at Amazon. You will work closely with I/O psychologists, economists, engineers, and business partners to estimate and validate models on large scale data, and turn the results of these analyses into products, policies, programs, and actions that have a major impact on Amazon’s business. We are looking for creative thinkers who can combine strong data science, software engineering, UX and product development skillsets with a desire to innovate, and who know how to execute and deliver on big ideas.Key Responsibilities· Develop of start-to-finish data product solutions from requirements gathering and ideation, through interface design and implementation.· Design data infrastructure and pipelines for machine learning and analytics products.· Obtain, merge, analyze, and report data using SQL, statistics software, and data visualization tools.· Apply various statistical and machine learning techniques to analyze large and complex data sets.· Communicate applied machine learning and statistic concepts to project sponsors, business leaders, and development teams across Amazon.· Understand business customer needs, iterate on feedback, and drive adoption
PL, Gdansk
Come and join the Database Migration Accelerator team that helps our customers migrate to the cloud. We are on a mission to transform legacy enterprise workloads into modern AWS native application architectures. We achieve this by utilizing cutting edge tools, sophisticated engineering systems and database expertise. We provide fixed price and high speed migrations to the cloud. Database Migration Accelerator is combining various AWS cloud platform services into one product which would serve our customers.We are a team of professionals that are forward-looking and using latest technology offerings (AWS cloud services, Machine Learning, Mathematical Optimization, Relational and NoSQL databases) to build new capability to operationalize and automate migration methodologies. Databases Services at AWS cover a range of data platforms including: Amazon Aurora, DynamoDB, Redshift, Athena, as well as AWS Database Migration Service, Data Pipeline, Glue and more. As each service grows, so does adoption by customers world-wide.We have an opportunity for a Senior Applied Scientist who is passionate about combining machine learning with developing new offerings for the cloud and is enthusiastic about applying bold new ideas to real-world problems.Joining the AWS Database Services team as a Senior Applied Scientist gives you the opportunity to:· Work for a company that’s at the forefront of the cloud computing space.· Be a part of something unique what no other previously developed and was successful.· Design machine learning solutions to intelligently move enterprises to the cloud.· Truly own the solution from concept design through development to production.· Join the team whose activities are regularly called out publicly by AWS CEO Andy JassyWork/Life BalanceOur team places value on work-life balance. Our team is global, based in the US and Poland. Our Poland teams typically start later in the day to have a couple of hours of overlap with US teams.Mentorship & Career GrowthOur team is dedicated to supporting new team members in an environment that celebrates knowledge sharing and mentorship. Our senior engineers mentor more junior engineers through one-on-one mentoring and collaborative code reviews. Projects and tasks are assigned in a way that leverages your strengths and helps you further develop your skillset.Inclusive Team CultureWe get to build a really cool service and the main contributing factor to our success is the inclusive and welcoming culture that we embody every day.We welcome teammates who are enthusiastic, empathetic, curious, motivated, reliable, and able to collaborate with a diverse team of peers.As a Senior Applied Scientist, your responsibilities will include:· Building new cloud based Machine Learning solutions and algorithms to accelerate migrations to the cloud· Participating in hands-on machine learning experimentation and delivering the results in the form of new products· Creating technical strategies and delivering with limited guidance· Solving difficult and complex software problems. Your solutions should be extensible· Cross-collaborating with a number of different teams with overlapping work, including solutions architects, developers, product managers, senior leaders, and many more· Mentoring more junior members of the team or collaboration partners