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
Are you passionate about the use of Machine Learning to improve the experience for Alexa and Smart Home customers? We have a team that is making revolutionary leaps forward in this space and are in need of a Machine Learning Scientist. You will have an enormous opportunity to impact the customer experience, design, architecture, and implementation of a new machine-learning driven product that will impact the lives of people you know every day.Great candidates for this position will have a passion for machine learning and signal processing and will have hands-on experience with product development. You will have the deep expertise to drive the ML vision for our products and technical breadth to make the right decisions about technology, models and methodology choices.As a Machine Learning Scientist at Amazon, you will connect with world leaders in your field working on similar problems. On this team you will analyze and model sensor data, smart home signals, and contextual data to create new experiences for customers. Meeting business requirements will involve combining several different machine learning algorithms with domain knowledge into sophisticated ML workflows. You will work with large distributed systems of data and will tackle Machine Learning challenges in Supervised, Unsupervised, and One-shot Learning, utilizing modern methods such as Deep Neural Networks and others. MLS’s have contributed to and are aware of the state-of-the-art in their respective field of expertise and are constantly focused on advancing the state-of-the-art for improving Amazon’s products and services.KEY RESPONSIBILITIES· Analyze and extract relevant information from large amounts of data to support new experiences for customers· Create novel ML approaches and apply them to achieve project goals· Build ML software and algorithms that cost-effectively scale to millions of customers· Work closely with other teams across Amazon to deliver platform features that require cross-team leadership· Ensure that the quality and timeliness of ML deliverables
IN, KA, Bangalore
What would you do if you had access to the world’s largest product catalog with billions of products, offers, images, reviews, searches, and much more? Amazon Selection and Catalog Systems is looking for an innovative and customer-focused applied scientist to improve the data quality of the world’s biggest product catalog, utilizing state-of-the-art machine learning techniques.An information-rich and accurate product catalog is a critical strategic asset for Amazon. It powers unrivaled product discovery, informs customers’ buying decisions, offers a large selection and positions Amazon as the first stop for our customers. Maintaining and improving the accuracy of product catalog is challenging due to sheer scale (billions of products in the catalog), diversity (products ranging from electronics to groceries to instant video across multiple languages) and multitude of input sources (millions of sellers contributing product data with different quality).You will conceive innovative solutions to measure and improve the quality of various aspects of our product catalog and influence the way millions of our customers discover and buy our products worldwide. The opportunity (puzzle to solve) is that there is no single solution as the problem scope is varied and diverse. The solutions you build will vary from simple rule based systems to machine learning, semantic analysis and text processing. You will have the opportunity to design new data analytical workflows at a scale rarely available elsewhere, utilizing state-of-the-art data science and machine learning tools such as Spark, Python, and Theano and Amazon’s cloud computing technologies such as Elastic Map Reduce (EMR), Kinesis, and Redshift. You will apply your knowledge in data science by creating algorithmic solutions that combine techniques such as clustering, pattern mining, predictive modeling, deep learning, statistical testing, information retrieval, and natural language processing and apply them to the voluminous data describing the products in the catalog and the customer interactions. You will evaluate with scientific rigor and provide inputs to business strategy and technical direction. You will collaborate with software engineering teams to integrate your algorithmic solutions into large-scale highly complex Amazon production systems.Responsibilities include:· Map business requirements and customer needs to a scientific problem.· Align the research direction to business requirements and make the right judgments on research/development schedule and prioritization.· Research, design, implement and deploy scalable machine learned models, including the application of state-of-art deep learning, to solve problems that matter to our customers in an iterative fashion.· Mentor and develop junior applied scientists and developers who work on data science problems in the same organization.· Stay informed on the latest machine learning, natural language and/or artificial intelligence trends and make presentations to the larger engineering and applied science communities.
IN, KA, Bangalore
What would you do if you had access to the world’s largest product catalog with billions of products, offers, images, reviews, searches, and much more? Amazon Selection and Catalog Systems is looking for an innovative and customer-focused applied scientist to improve the data quality of the world’s biggest product catalog, utilizing state-of-the-art machine learning techniques.An information-rich and accurate product catalog is a critical strategic asset for Amazon. It powers unrivaled product discovery, informs customers’ buying decisions, offers a large selection and positions Amazon as the first stop for our customers. Maintaining and improving the accuracy of product catalog is challenging due to sheer scale (billions of products in the catalog), diversity (products ranging from electronics to groceries to instant video across multiple languages) and multitude of input sources (millions of sellers contributing product data with different quality).You will conceive innovative solutions to measure and improve the quality of various aspects of our product catalog and influence the way millions of our customers discover and buy our products worldwide. The opportunity (puzzle to solve) is that there is no single solution as the problem scope is varied and diverse. The solutions you build will vary from simple rule based systems to machine learning, semantic analysis and text processing. You will have the opportunity to design new data analytical workflows at a scale rarely available elsewhere, utilizing state-of-the-art data science and machine learning tools such as Spark, Python, and Theano and Amazon’s cloud computing technologies such as Elastic Map Reduce (EMR), Kinesis, and Redshift. You will apply your knowledge in data science by creating algorithmic solutions that combine techniques such as clustering, pattern mining, predictive modeling, deep learning, statistical testing, information retrieval, and natural language processing and apply them to the voluminous data describing the products in the catalog and the customer interactions. You will evaluate with scientific rigor and provide inputs to business strategy and technical direction. You will collaborate with software engineering teams to integrate your algorithmic solutions into large-scale highly complex Amazon production systems.Responsibilities include:· Map business requirements and customer needs to a scientific problem.· Align the research direction to business requirements and make the right judgments on research/development schedule and prioritization.· Research, design, implement and deploy scalable machine learned models, including the application of state-of-art deep learning, to solve problems that matter to our customers in an iterative fashion.· Mentor and develop junior applied scientists and developers who work on data science problems in the same organization.· Stay informed on the latest machine learning, natural language and/or artificial intelligence trends and make presentations to the larger engineering and applied science communities.
US, CA, Cupertino
Are you a biochemistry research scientist? At Amazon, we are constantly inventing and re-inventing to be the most customer-centric company in the world. To get there, we need exceptionally talented, bright, and driven people. We are a smart team of doers that work passionately to apply cutting-edge advances in technology and to solve real-world problems that will transform our customers’ experiences in ways we can’t even imagine yet.As a Research Scientist, you will be working with a unique and gifted team that is developing exciting products and collaborating with cross-functional teams.Responsibilities:· Collaborate to define product specifications and protocols· Iterate through experimentation to identify optimal product parameters· Identify and qualify new materials· Ensure manufacturability across the design process· Contribute to design control and regulated protocols· Collaborate with engineering teams to design, implement, and harmonize solutions
US, WA, Seattle
** LOCATION CAN BE EITHER TEMPE, SEATTLE OR NASHVILLE**Amazon Transportation Services (ATS) Line Haul is looking for a talented Data Scientist who will own analytics and develop solutions to drive insights and optimization for Network Planning and Forecasting. As a member of this team, you will have an opportunity to be an innovator in Amazon Logistics and work with a group of talented program managers, product managers, research scientists, software developers, and business stakeholders to design the Amazon network of the future.This position requires innovative thinking, ability to quickly approach large ambiguous problems, technical and engineering expertise to rapidly research, validate, visualize, prototype and deliver solutions. This position also requires significant cross functional work and integration with transportation, tech, operations and finance. Successful candidates will thrive in a fast-paced environment.As you further your career as a Data Scientist at Amazon, you will focus on improving corporate reporting frameworks and data visualization. You will examine performance data, discover and solve real world problems related to forecasting, and build critical metrics and business cases. We are focused on your success and want to build and support strong pioneers within Amazon Transportation Services. You can expect to leverage your problem solving skills and have full ownership of the projects you work on.Responsibilities:· Perform complex data research to identify opportunities to reduce fulfillment costs as well as improve efficiencies and customer experience· Design, develop and establish KPIs to provide strategic insights to drive growth and performance· Ability to perform/own reoccurring and ad-hoc business intelligence projects, including the development of advanced statistical models that improve the forecasting capabilities of the Amazon transportation network· Develop standardized metrics to evaluate and benchmark pertaining to short and long term network planning and forecasting· Communicate complex insights to stakeholders, both verbally and in writing
US, NY, New York
Amazon Web Services is looking for world class scientists to join the research team within AWS Security Services. You would be entrusted with researching and developing core data mining and machine learning algorithms for various AWS security services like GuardDuty (https://aws.amazon.com/guardduty/) and Macie (https://aws.amazon.com/macie/). On this team, you will invent and implement innovative solutions for never-before-solved problems. If you have passion for security and experience with large scale machine learning problems, this will be an exciting opportunity.The AWS Security Services team builds technologies that help customers strengthen their security posture and better meet security requirements in the AWS Cloud. The team interacts with security researchers to codify our own learnings and best practices and make them available for customers. We are building massively scalable and globally distributed security systems to power next generation services.Key Responsibilities:· Rapidly design, prototype and test many possible hypotheses in a high-ambiguity environment, making use of both quantitative and business judgment· Collaborate with software engineering teams to integrate successful experiments into large scale, highly complex production services.· Report results in a scientifically rigorous way· Interact with security engineers and related domain experts to dive deep into the types of challenges that we need innovative solutions for
US, WA, Seattle
The Central AWS Econ team is dedicated to bringing the most trustworthy evidence-based analysis to the most strategic decisions for AWS leadership.Our studies impact strategic investments, service business model, resource allocation, product priorities and pricing models, go-to-market motions and more.This Senior economist role partners with AWS business leaders across the organization to define and deliver on economic questions that guide their most strategic decisions. The successful candidate will be a problem solver who enjoys diving into data, is excited by difficult modeling challenges and ambiguous starting points, and possesses strong communication skills to effectively interface and collaborate with product, finance, planning and business teams.Specific questions include developing supporting economics for new business model, evaluating the relationship between short and long term growth, mapping and affecting the customer journey through different AWS products and cloud technologies.The Central AWS Econ team is dedicated to answering these (and many more) questions using quantitative, economic and statistical methods.Key Responsibilities:· Identify and propose impactful economic studies based on business meetings· Lead / conduct economic studies, including developing and communicating practical implications to senior leadership· Mentor and develop junior economists and data scientists· Develop new repeatable data analysis pipelines to be used by non-economists
US, WA, Seattle
Amazon delights millions of customers around the world. Meet PI-Squared, the behind-the-scenes team, that enables our HR and Operations Leaders to make informed decisions and improve the overall experience of a million frontline employees and leaders throughout their journey at Amazon. Our diverse team of statisticians, machine learning experts, and social scientists strive to make Amazon HR the most scientific HR organization in the world. We form hypotheses about the best talent acquisition, talent retention, and talent development techniques, and then set out to prove or disprove them with experiments and careful data collection.The ambition of Amazon HR is to be the most scientific organization in the world. We bring data and machine learning into management science to deliver workforce, associate experience, and leadership insights so Amazon leaders can focus their efforts in ways that will engage, retain and grow their talents. You will have the opportunity to work with operation leaders across different business lines to gain deep insights into Amazons’ daily operation and directly impact productivity, quality, and safety of hundreds of thousands of employees’ everyday life.Roles and Responsibilities:(1) Undertake econometric / statistical analysis to measure impact of various initiatives in the HR space.(2) Design and measure experiments(3) Undertake qualitative analysis to augment the findings from quantitative studies(4) Build scalable analytic solutions using state of the art tools based on large datasetsThis role requires an individual with strong quantitative modeling skills and the ability to apply statistical/machine learning, econometric, and experimental design methods. Preference will be given to candidates with additional experience in qualitative analysis in a variety of settings such as focus groups, field studies, surveys, and observational studies.
US, WA, Seattle
Try Before You Buy (TBYB) team at Amazon Fashion is looking for an Applied Scientist to join us to build our next-generation personalized recommendation systems for Personal Shopper and Prime Wardrobe. In this role, you will be responsible for researching, developing, and deploying machine learning, computer vision, and NLP models to make customers' fashion shopping experience at Amazon engaging and joyful.The primary responsibilities of this role include:· · Build ETL pipelines to collect and process data· · Frame and transform ambiguous business challenges into science hypotheses. Design and implement offline and online experiments to evaluate them· · Develop prototypes to test new concepts/proposals for models and algorithms· · Design and build automated, scalable pipelines to train and deploy ML models