Source: thenewstack.io
Amazon Web Services Brings Machine Learning to DataOpsCategory: Database, Data, Infrastructure, automation, artificial-intelligence
Well, Amazon Web Services is hoping to bring more machine learning to the worldwide development community. Over the past few years, Amazon Web Services has exerted a lot of engineering effort into integrating the processes around creating and refining machine learning models into modern development lifecycles, developing a platform, Amazon SageMaker, to streamline the process.
You prepare some of the data, you train the model and you [check] the model is converging the right way.
Amazon Redshift ML works with Amazon SageMaker Autopilot, a service that automatically trains and tunes the best ML models for classification or regression based on your data while allowing full control and visibility.When you run an ML query in Amazon Redshift, the selected data is securely exported from Amazon Redshift to Amazon Simple Storage Service (Amazon S3).
As a result, you can now create, train, and apply ML on Neptune data in hours instead of weeks without the need to learn new tools and ML technologies.
You prepare some of the data, you train the model and you [check] the model is converging the right way.
Amazon Redshift ML works with Amazon SageMaker Autopilot, a service that automatically trains and tunes the best ML models for classification or regression based on your data while allowing full control and visibility.When you run an ML query in Amazon Redshift, the selected data is securely exported from Amazon Redshift to Amazon Simple Storage Service (Amazon S3).
As a result, you can now create, train, and apply ML on Neptune data in hours instead of weeks without the need to learn new tools and ML technologies.
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