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Python, R, Java, JavaScript, TensorFlow, PyTorch, Apache MXNet, Hugging Face Transformers, Amazon SageMaker, AWS Deep Learning AMIs and Containers, Jupyter Notebooks, Amazon S3, EFS, FSx, Glacier, Aurora, DynamoDB, Neptune, OpenSearch, APIs and SDKs for easy integration into applications, Amazon EC2.
 AWS ML services allow businesses across industries to build, deploy, and manage cloud-based machine learning solutions using pre-built tools and frameworks on the Amazon Cloud infrastructure. Integrate a smart, intelligent, and secure ML solution in your business apps for a transformative user experience.
End-to-end encryption, IAM-based access controls, and compliance with HIPAA, GDPR, SOC, and ISO standards are ensured by AWS for ML workloads when it comes to integration with existing business apps or while building from scratch.Â
 It’s highly cost-effective as you can leverage the pay-as-you-use model of Amazon ML services to build your business solutions and reduce technology costs significantly for your business. Lower your operational costs and optimize company expenses in a better manner.Â
 Tools like SageMaker endpoints, Kinesis streaming, and serverless Lambda triggers enable AWS machine learning services to handle real-time data processing by supporting both batch-wise and auto-scaled data management.
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