AWS has the broadest and deepest set of machine learning and AI services for your business.
From the world's largest enterprises to emerging start-ups, more machine learning is built on AWS than anywhere else.
AWS customers use machine learning to improve the quality of healthcare, fight human trafficking, provide better customer service, and protect you from fraud. With the broadest and deepest set of machine learning and AI services, they are creating new insights, enabling new efficiencies, and making more accurate predictions. That's why more than 10,000 customers have chosen to use AWS for machine learning.
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Dive deeper, develop an effective strategy, and start leveraging the full power of machine learning technologies.
The Machine Learning Journey
The lack of a singular proven path to machine learning success is making businesses unsure of how to take the next (or even the first) step on the journey.
This e-book is designed to help businesses forge ahead, outlining a proven path from first step to measuring results—with insights from Amazon’s own machine learning heritage and its experience helping thousands of customers realize their own initiatives.
Read this e-book to learn more about:
- Path to unlock the power of machine learning
- Machine learning challenges and roadblocks, and steps to overcome
- Real-world applications
- Taking the next steps with AWS
The TCO of Amazon SageMaker
In this e-book, we look at how Amazon SageMaker, an end-to-end machine learning service to build, train, and deploy models at scale is improving data scientist productivity by up to 10x; helping customers accelerate their ML initiatives from POC to Production; and significantly reducing the total cost of ownership relative to other cloud-based ML solutions.
Learn how you can achieve 54% lower TCO compared to other cloud based ML options – over a 3-year horizon, using Amazon SageMaker.
Read this e-book to learn more about:
- TCO of Amazon SageMaker from building, training, to deployment
- How others maximize cost-saving, productivity, and efficiency
- Applications and how to determine your TCO
Implementing AI: From Exploration to Execution
AI and Machine Learning are widely recognized as a transformative set of technologies that will fuel competitive advantage, innovation, and growth. How are leaders in AI adoption identifying their most promising use cases and choosing the most appropriate technology infrastructure? How are they confronting implementation challenges?
Read on to get the best practices, use cases, and learnings from the leaders in AI adoption.
Read this e-book to learn more about:
- Best practices and learnings from early adopters of AI
- Opportunities, case studies, and industrial applications of AI
- How to scale and integrate AI cross the organization
Enterprise Machine Learning Guide
According to Gartner, the global business value derived from artificial intelligence (AI) is projected to reach $3.9 trillion in 2022.
Machine learning presents new opportunities to realize foundational gains such as efficiency and cost saving, as well as higher-value gains such as product innovation and spurring discovery and research. But how do organizations get started?
Read on to learn how you can leverage machine learning to meet existing and future business and IT needs.
Read this e-book to learn more about:
- Defining your use cases for machine learning innovation
- Overcoming barriers in machine learning adoption and how to scale across the organization
- Real-world applications and business outcomes
AWS helps you move faster, operate more securely, and save substantial costs; all while benefitting from the agility, scale and performance of the cloud.
Using AI and machine learning built on AWS, the NFL will generate new insights into player injuries, game rules, equipment, rehabilitation, and recovery.
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ANZ bank built a chatbot using Amazon Lex to help employees get answers to policy information quickly, efficiently, and in an engaging manner.
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With AWS, USG Boral developed an intelligent warehouse safety system to prevent accidents – one of the firsts in the manufacturing industry in APAC.
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Policybazaar.com processed more than 150,000 customer calls, had 80% of calls answered, and 41% of sales closed without the interference of agents.
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Kia uses AWS machine learning services to personalize driver experience, improve safety, and reduce fatalities from auto accidents.
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With AWS AI services, BGL eliminates transaction errors, boosts productivity, and reduces administration time and costs by 30%.
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A spatial data platform company in South Korea, Urbanbase uses Amazon SageMaker to achieve 100x accelerated development, 20x faster services launch, and lower deep learning costs.
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F1 uses Amazon SageMaker to build ML models that help fans better understand the split-second decisions made by a driver or pit crew that can dramatically affect the outcome.
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Sunday uses ML algorithms to offer customized premiums that deliver greater value and 10-20% cost savings to customers; all while achieving 30% monthly revenue growth.
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Editorji, a Delhi-based startup, builds AI-powered news app on AWS and seamlessly scales up to 300% during high-demand periods.
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Airtasker builds all its machine learning solutions on AWS – such as tools for automation, fraud detection, customer service, and task classification.
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Amazing Fables, a startup based in Malaysia, brings stories to life in 10 different languages, and operates with a lean team of 3, by using AWS machine learning solution.
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TuSimple, based in San Diego and China, uses AWS to build self-driving trucks. By the end of 2020, TuSimple aims to remove human fail-safe.
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Siemens handles 60,000 cyber threats per second using AWS machine learning services – and the solution is developed and managed by only 12 employees.
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Amazon SageMaker has helped redBus increase their conversion rate by 5%, click-through rate by 20% and reduce the time to market from months to weeks.
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