Dive deeper, develop an effective strategy, and start leveraging the full power of machine learning technologies.
AI use cases
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
Think of it as an instruction manual to guide you on your journey at every stage and help you accelerate machine learning initiatives to achieve measurable results.
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, and 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
Download the e-book now to start or expand your machine learning journey.
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 e-book to learn why hundreds of thousands of organizations use AWS ML to help them:
- Improve customer experiences
- Optimize business operations
- Accelerate innovation
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
Read the e-book to learn how security features from Amazon SageMaker and the AWS Cloud can help you go from idea to production faster.
Tackling our world's hardest problems with machine learning
With the evolution of machine learning technologies, industries and categories are now using the power of machine learning for the benefit of society. Learn how innovators are tapping into the best practices, in-depth expertise, and powerful solutions of companies like AWS to launch new initiatives and solutions that are improving lives and protecting our planet right now, including:
- Advances in medicine
- Supporting our veterans
- Housing the homeless
- Reducing carbon emissions
A strategic playbook for data, analytics, and machine learning
If your organization is like most others, the pressure is on to use data, analytics, and machine learning to drive critical business decisions, including:
- When to expand product offerings
- How to introduce new revenue streams
- Where to automate manual processes
- How to optimize interactions with customers and business partners
Download the IDG CIO Playbook for help refining your data-driven strategy so you can effectively scale analytics and machine learning across your enterprise. Get the guidance you need to accelerate innovation and drive your business forward with data.
The AI & machine learning imperative
Machine learning is transforming how businesses innovate with AI. Still, many leaders find it difficult to sift through the market hype in search of meaningful value. This guide can help decision makers at every stage of the journey confront and resolve challenges across the three key levels of AI adoption: leadership, organization, and talent.
Accelerate machine learning innovation with the right cloud services and infrastructure
Machine learning has entered the mainstream, powering a wide range of benefits. Discover how you can build a solid foundation for machine learning success at your organization through more accurate forecasting, personalized customer experiences, and greater operational efficiency.
Read more to learn about purpose-built services and powerful infrastructure from AWS can help you accelerate every step of the machine learning lifecycle:
- Prepare data for machine learning quickly and easily
- Build accurate machine learning models across multiple frameworks
- Train machine learning models faster
- Deploy machine learning models at the lowest possible cost
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 e-book, 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
Q&A: Choosing the right compute infrastructure for machine learning
Now that the cloud has brought machine learning within reach for organizations big and small, many executives are asking, “What factors should I consider when choosing the right machine learning infrastructure and services for my objectives?” For the answers to that question and more, we turned to Dr. Saha, Vice President of machine learning services in Amazon AI. Access the Q&A for a closer look at:
- The biggest challenges facing developers and organizations as they adopt machine learning
- Guidance and best practices for evaluating the infrastructure requirements of your ML workloads
- Amazon EC2 compute instances for ML model training and inference
Realize superior business outcomes, developer efficiency, and accelerate innovation with high-performance, cost-efficient, and easy-to-use machine learning infrastructure
IDC forecasts that spend on infrastructure to power AI/ML solutions will increase from $14.6 billion in 2020 to about $30.5 billion in 2024.
Read this IDC whitepaper for a closer look at the most common challenges in deploying your AI/ML applications and learn what makes AWS an ideal partner for ML application development and deployment at scale. Discover how your organization can benefit from:
- New high-performance and cost-efficient Amazon EC2 DL1 instances powered by Gaudi accelerators by Habana Labs, for training deep learning models
- Getting started easily with powerful managed services such as Amazon SageMaker
- A lower barrier to entry to using the cloud to scale ML applications and innovate faster
AI use cases
IDC MarketScape: Asia Pacific Vision Artificial Intelligence Software Platform 2021 Vendor Assessment
AWS has been named a Leader in the inaugural IDC MarketScape: Asia Pacific (Excluding Japan) Vision AI Software Platform 2021 Vendor Assessment. The IDC MarketScape evaluated eight vendors across APEJ (Asia Pacific excluding Japan) and named AWS as a Leader in strategies and capabilities. In this excerpt, discover why the IDC MarketScape positioned AWS a leader and why the IDC MarketScape recommends computer vision decision makers should consider AWS for Vision AI services when there's a need to centrally plan vision AI capabilities in a large-scope initiative, such as digital transformation (DX) or when customers are seeking Composite capabilities for AI, data management, and cloud compute, with more availability of cloud regions.
Read the excerpt to:
- Understand and gain insights into the computer vision landscape across APEJ including emerging trends, adoption patterns, and new vision AI uses case in demand
- Assess the strengths and cautions of AWS and its comprehensive services
- Plan for the future with recommendations from the IDC MarketScape on when to consider AWS for computer vision services
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. The result is fractured communications and limited visibility into customer needs.
Machine learning can help you offer more relevant customer recommendations and ultimately improve brand loyalty. Read the e-book 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
- How a company used personalization with machine learning to increase video consumption by 20%
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 e-book 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 free
Escape from manual document processing and find freedom in a machine learning solution. Work more efficiently with intelligent document processing.
Read the e-book 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
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. Knowledge is key to business insights, and there are hard costs and risks associated with time spent searching for information or faulty decisions made based on inaccurate information.
Intelligent search powered by machine learning can help solve these challenges by leveraging natural language understanding and deep learning. Read the e-book to identify areas for organizational transformation, including:
- Increase workforce productivity
- Improve customer self-service and satisfaction
- Accelerate research and development
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 e-book 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 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.