AWS Machine learning

AWS artificial intelligence services enable enterprises to apply machine learning, natural language processing and other advanced technologies to their applications. Open up a vast number of computing possibilities with these AWS AI services and tools.

Machine learning relies on complex models that developers must train and tweak in response to expansive reservoirs of real-world data. This process can be painfully slow, expensive and filled with complications. Amazon SageMaker is a managed service intended to alleviate much of that complexity, but how exactly can enterprises use it?

Customer support

Enterprise are now using machine learning to monitor data about their customers, develop more accurate pictures of consumer preferences

Predictive maintenance

works by monitoring data streams generated by equipment. This sets a baseline profile describing normal operations

Industrial automation and process automation

Automation is often seen as one of the main reasons to adopt machine learning.

Forecasting

Machine learning can sharpen forecasting in a range of industries.

Machine Learning with AWS :

How Amazon SageMaker works

The developers can launch a pre-built notebook, which AWS supplies for a variety of applications and use cases, then customize it according to the data set and schema the developer wants to train. Developers can also use custom-built algorithms written in one of the supported ML frameworks or any code that has been packaged as a Docker container image. SageMaker can pull data from Amazon Simple Storage Service (S3), and there is no practical limit to the size of the data set.

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