Machine Learning on AWS
AWS has a vast and profound set of machine learning and AI services for your business.
They are focused on solving some of the worst challenges that hold back ML from every developer.
You can decide from pre-trained AI services for computer vision, language, recommendations, and forecasting; Amazon Sage-Maker to quickly build, train and deploy ML models at scale; or build custom models with assistance for all the popular open-source frameworks.
Their capabilities are built on the most extensive cloud platform, optimized for machine learning with high-performance computing, and no trade-offs on security and analytics.
Amazon SageMaker enables data scientists and developers to quickly and easily build, train, and deploy ML models at any scale. It eliminates the complexity that gets in the way of executing machine learning successfully across various use cases and industries—from running models for real-time fraud detection to virtually analyzing biological impacts of potential drugs, to predicting stolen-base victory in baseball.
No machine learning skills required
AWS pre-trained AI Services render a ready-made intelligence for your applications and workflows. AI Services effortlessly integrate with your applications to address traditional use cases such as personalized suggestions, improving your contact center, advancing safety and security, and enhancing customer engagement. Because they use the identical deep learning technology that powers Amazon.com and their ML Services, you get quality and accuracy from continuously-learning APIs. And best of all, AI Services on AWS don’t require ML expertise.
Select from TensorFlow, PyTorch, Apache MXNet, and other popular frameworks to test with and customize machine learning algorithms. You can apply the framework of your preference as a managed experience in Amazon SageMaker or use the AWS Deep Learning AMIs (Amazon machine images), which are entirely configured with the latest versions of the most successful deep learning frameworks and tools.
- 81% of the Deep learning projects in the cloud run on AWS
- 85% of the TensorFlow projects in the cloud run on AWS
- Fastest training for popular deep learning models: AWS-optimized TensorFlow and PyTorch is recorded as the fastest training time for Mask-RCNN (object detection) and BERT (NLP).
Get the right to compute for any use case
Leverage a wide set of powerful compute choices, varying from GPUs for compute-intensive deep learning to FPGAs for specialized hardware acceleration, to high-memory cases for managing inference. Amazon EC2 presents a wide selection of case types optimized to fit machine learning use cases, whether you are training models or running inference on trained models.
- 3x faster network throughput compared to other providers using P3dn instances
- 25% improvement in cost and performance using C5 instances powered by 3.0GHz Intel Xeon compared to previous generation instances
- Custom hardware acceleration utilizing F1 instances with field-programmable gate arrays (FPGAs)
- Powerful performance and the most economical machine learning inference in the cloud with Inf1 instances
Analytics and Security
Analytics and security for machine learning
To perform machine learning successfully, you not only need machine learning abilities, but also the right security, data store, and analytics services to work collectively. With AWS, you get the most extensive capabilities to support your machine learning workloads.
- 99.999999999% stability and unmatched availability using Amazon S3 and Amazon S3 Glacier for storage
- Up to 400%, faster data queries while using Amazon Redshift for analytics
- A most extensive set of security & encryption capabilities
AWS DeepRacer is a fully self-sufficient 1/18th-scale race car designed to aid you to learn about reinforcement learning through independent driving. Now you can feel the thrill of the race in the real world when you deploy your RL model onto AWS DeepRacer
AWS DeepLens is the world’s 1st deep learning-enabled video camera for developers. It is integrated with Amazon SageMaker and many other AWS services. It allows you to start with deep learning in less than 10 minutes through sample projects with practical, hands-on examples.
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