{"id":946,"date":"2019-09-20T14:08:40","date_gmt":"2019-09-20T12:08:40","guid":{"rendered":"https:\/\/blog.besharp.it\/?p=946"},"modified":"2021-03-24T17:59:08","modified_gmt":"2021-03-24T16:59:08","slug":"machine-learning-on-aws-how-to-create-and-deploy-a-ml-backed-service-with-aws-sagemaker","status":"publish","type":"post","link":"https:\/\/blog.besharp.it\/machine-learning-on-aws-how-to-create-and-deploy-a-ml-backed-service-with-aws-sagemaker\/","title":{"rendered":"Machine Learning on AWS: How to create and deploy a ML backed service with AWS SageMaker"},"content":{"rendered":"

In the last decade, the way we deal with and manage information dramatically changed due to two main reasons: on the one hand, the cost of data storage is becoming lower and lower,<\/strong> mainly due to the broad adoption and spread of public cloud services; on the other, thanks to the ubiquitous use of ERPs, CRMs, IoT platforms, and other monitoring and profiling software, a huge amount of data has become available<\/strong> to companies, both about their internal processes and about customer preferences and behaviors. So, basically, we have the opportunity to deal with more and more data, with an always increasing data quality<\/strong>, at a fraction of the cost.<\/span><\/p>\n

The availability of these big datasets to be analyzed and mined for information sparked a new interest in Artificial Intelligence<\/strong> and particularly Machine Learning\u00a0 (ML),<\/strong> which can be used to extract a predictive model from an existing dataset.<\/span><\/p>\n

The rise of internet integrated devices (IoT) dramatically increased the rate at which data are created and stored and the availability of these data, combined with new ML techniques,\u00a0 in turn, gives rise to a plethora of novel possibilities which would have been unthinkable just a few years ago. For example it is now possible to know how customers are using the products, which mistakes or unintended operations they are doing, which parts of a device wear out first in a real-world situation, which components of an industrial machine are more likely to fail given usage time and sensor readings (predictive maintenance), understand automatically if a produced part is good or faulty based only on the images of the given component and on a huge collections of images of good and faulty components.<\/span><\/p>\n

The ability to correctly extract, interpret and leverage the information<\/strong> contained in the collected data is thus a huge added value and a competitive advantage for the companies who undertake the often significant effort to develop it.\u00a0<\/span><\/p>\n

Cloud providers, such as Amazon Web Services AWS,<\/strong> nowadays offers a wide range of Machine Learning centered services in order to meet the most common use cases of customers. On AWS currently, these ML backed services are available:<\/span><\/p>\n