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AWS eBooks
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AI/ML Use Cases
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AWS eBooks
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The machine learning journey
Businesses have the opportunity to unlock significant value across the organization with
the help of machine learning and AI. Follow the proven path to machine learning success. Read the e-book to discover:- The full machine learning journey in 6 steps
- How to transform investments into business-differentiating solutions
- Inspiring stories of industry leaders who’ve successfully implemented machine learning
- Insights from AWS machine learning experts
7 leading machine learning use cases
Move beyond the hype and discover the tangible benefits of machine learning. In this e-book, we have outlined seven leading use cases where businesses have successfully applied machine learning to achieve fast, efficient, measurable results. Read the e-book to learn more about these use cases and the requirements you should look for when identifying a suitable application for machine learning, such as:
- Solves real business problems
- Leverages sources of untapped data
- Can be completed in a matter of months
Accelerating machine learning innovation through security
To build successful machine learning models, you often need datasets unique to your business. These datasets are extremely valuable assets and need to be secured throughout every step of machine learning—including data preparation, training, validation, and inference. Amazon SageMaker, a fully managed machine learning service, provides comprehensive security features that can help your organization:
- Meet the strict security requirements of machine learning workloads
- Secure datasets through every step of the process
- Go from idea to production faster, more securely, and with a higher rate of success
Achieving transformative business results with machine learning
By providing the broadest and most complete set of machine learning (ML) services, AWS is able to meet its customers wherever they’re at in their ML journeys and help them achieve specific objectives. Read the AWS ML Customer Look book to learn why hundreds of thousands of organizations use AWS ML to help them:
- Improve customer experiences
- Optimize business operations
- Accelerate innovation
Modernize machine learning development at scale
Machine learning has entered the mainstream and organizations are leveraging its power to drive real-world innovation. Amazon SageMaker, the most comprehensive ML cloud service, provides scalable infrastructure and integrated tools to enable organizations modernize their ML development and reap the benefits of ML faster. In this eBook discover the business outcomes of modernizing ML development using Amazon SageMaker including:
- Acceleration of ML development
- Responsible and secure use of ML
- ML accessibility to builders of all ML skill levels
- Reduction in the total cost of ownership
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AI/ML Use Cases
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Seven reasons why your enterprise needs intelligent search
Organizations are hungry to use data to grow and improve performance, but enterprises are struggling with search today. Intelligent search powered by machine learning can help solve these challenges by leveraging natural language understanding and deep learning. Read 7 Reasons Why Your Enterprise Needs Intelligent Search to identify areas for organizational transformation, including:
- Increase workforce productivity
- Improve customer self-service and satisfaction
- Accelerate research and development
Drive business growth with personalization
Personalizing content for a customer online is key to breaking through the noise. Yet brands face challenges that prevent them from providing these seamless, relevant experiences. Machine learning can help you offer more relevant customer recommendations and ultimately improve brand loyalty. Read Drive Business Growth with Personalization to learn:
- How machine learning can overcome challenges with traditional personalization efforts to increase engagement and conversion
- How industries like Media and Entertainment (M&E) and Retail are leveraging personalization powered by machine learning
Improving service and reducing costs in contact centers
It’s time to leave behind the challenges of legacy contact centers—long wait times, misdirected calls, and resolution delays. Instead, deploy the power of machine learning and give customers the solutions they need. Read the eBook Improving Service and Reducing Costs in Contact Centers to learn how you can leverage AWS AI services to:
- Reduce costs with self-service technologies
- Increase agent productivity by reducing call volume
- Identify business improvement opportunities by capturing better interaction data
- Establish proficient call centers and create great customer experiences
Set your document data free
Escape from manual document processing and find freedom in a machine learning solution. Work more efficiently with intelligent document processing. Read the eBook Set your document data free to learn how you can use machine
learning to:- Curtail or even eliminate errors associated with manual data entry and processing
- Enable faster document data processing to accomplish what once took months or weeks in a matter of days
- Improve customer satisfaction by providing your clients with more accurate information faster and more efficiently
- Boost productivity by helping workers spend more time on business-critical tasks
Why machine learning is essential in your fight against online fraud
Any organization that conducts business online can fall victim to online fraud and must seriously consider the risk of revenue loss and damage to the brand. AWS is helping businesses like yours fight back. Read the eBook Why machine learning is essential in your fight against online fraud to learn how you can:
- Develop your own solution in just days with Amazon SageMaker.
- Integrate Amazon Fraud Detector with your business applications using an API
Learn more about Machine Learning on AWS
Leader in Gartner Magic Quadrant for Cloud AI Developer Services
Featured Customers
Learn how these organizations are accelerating business outcomes with artificial intelligence and machine learning.
nib Group automates claim processing using AWS machine learning solutions, increasing self-service rate by 30% freeing up employees to focus on more complex cases.
Globe Telecom builds a robust 360-degree customer view using AWS, onboarding 40x more user attributes a month.
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Using Amazon SageMaker, Hyundai achieves 10x faster model training speed with just five times as many instances for autonomous driving.
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codemantra helps businesses improve digital document accessibility with AI-powered platform on AWS, cutting document processing time by 95%.
Amazon Robotics uses Amazon SageMaker, saving 50% on ML inferencing costs, improving computing performance by 40%, and boosting 20% in productivity improvement.
HirePro runs AI algorithms on AWS to deliver seamless virtual hiring, reducing overall time-to-hire by 75%.
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CreditorWatch serves 55,000+ customers with a data analytics team of 4 and processes 400 billion data points in 8 hours by running on AWS.
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Portcast scales machine learning models at sea using Amazon SageMaker, cutting ML deployment costs by 60%.
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Using Amazon Forecast for prediction, PureTech Global is able to optimize billing revenue seeing a 20% increase in bill renewal success rate.
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F1 Insights series powered by AWS bring fans closer to the split-second decisions on the track.
Pomelo Fashion enhances shoppers experience and increases revenue using Amazon Personalize, seeing a return of investment by 400% within a month.
SLA Digital uses Amazon Fraud Detector to shape the future of secure transactions and was able to cut development time for each fraud detection model from months to weeks.
Perfios processes over 2 million financial documents per month using Amazon Textract, saving 12 months on algorithm development time.
PredictHQ runs its ML models using Kubernetes on AWS to perform distributed data analysis at scale.
DENSO automates machine learning model development for driving support using Amazon SageMaker, reduces work time by 55-66%.
Games24x7 uses machine learning on AWS to personalize the gaming user experience, expanding the user base by 400% in 2 years.
Urbanbase launches services 20x faster and accelerated development 100x by using Amazon SageMaker.
See-Mode improves stroke detection and prevention with machine learning on AWS, processing 50-100 ultrasound images within seconds.
Dream11 runs multiple machine learning solutions on AWS, scaling and expanding user base from 2 million to more than 100 million in 4 years.
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