SageMaker integration
Amazon SageMaker is AWS’s primary service for ML development. It provides a set of tools and features that lets users handle all the stages of the ML development pipeline, from data collection and preparation to model deployment and hosting.
Just like any other ML tool, SageMaker relies on the concept of model training to get models up to the accuracy level expected from them. And as we mentioned previously, training ML models usually requires large amounts of data to be prepared and processed. Because of this, SageMaker offers native integration with Apache Spark (https://docs.aws.amazon.com/sagemaker/latest/dg/apache-spark.html), which provides model-training capabilities using an AWS-tailored version of Spark.
One of the most important features SageMaker offers is serverless notebooks (https://docs.aws.amazon.com/sagemaker/latest/dg/nbi.html). A notebook instance is a serverless EC2 instance that runs Jupyter (https://jupyter.org), a web-based...
