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Learning AWS - Second Edition by Amit Shah, Aurobindo Sarkar

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Using Amazon SageMaker for machine learning

Typically, the machine learning process comprises the following steps:

  • Data wrangling: This involves setting up and managing notebook environments. This will help you to get data to notebooks securely.
  • Experimentation: This involves setting up and managing clusters. This will help you scale/distribute ML algorithms.
  • Deployment: This involves setting up and managing inference clusters. This will help you manage and autoscale inference APIs with testing, versioning, and monitoring.

The preceding cycle can take as long as 6 to 18 months. However, given the potential of machine learning and AI to empower and improve businesses, the effort is often worthwhile. Challenges in large-scale machine learning ...

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