Machine Learning - eBook (EN)

ML Six steps to machine learning success

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ML frameworks: AWS customers can choose from TensorFlow, PyTorch, Apache MXNet, and other popular frameworks to experiment with and customize ML algorithms. You can use the framework of your choice as a managed experience in Amazon SageMaker, or use the AWS Deep Learning AMIs (Amazon Machine Images) and AWS Deep Learning Containers, which are fully configured with the latest versions of the most popular deep learning frameworks and tools. Amazon Elastic Compute Cloud (Amazon EC2) provides a wide selection of instance types optimized to fit ML use cases—regardless of whether customers are training models or running inference on trained models. These instances range from GPUs for compute-intensive deep learning training to AWS Inferentia for low-cost inference. Implementation support: The Amazon Machine Learning Solutions Lab pairs your team with ML experts to help you identify and build ML solutions that address your organization's highest ROI ML opportunities. We also offer training to augment the level of ML expertise on your team, including developer training, business leader training, and a hands-on event through the AWS Machine Learning Embark Program. Learn more about how you can transform the responsible use of AI and ML from theory into practice with purpose-built services, resources, and training. Learning tools: You can improve your ML capabilities with in-depth learning tools, including: • AWS DeepRacer • Machine Learning Training and Certification • Amazon Machine Learning Solutions Lab • Amazon SageMaker Studio Lab Machine learning with AWS, by the numbers 100,000+ customers are using AWS for their AI and ML workloads 20+ years of building experience at Amazon Up to 10x improvement in data scientists' productivity Hundreds of algorithms and models in Amazon SageMaker JumpStart 21

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