Implementing analytics on Amazon SageMaker

Amazon SageMaker is a fully managed service that enables data scientists to build, train, and deploy ML models at any scale. Amazon SageMaker is based on Jupyter Notebook so that developers can use a familiar user interface to build their own analytics. The basic concepts of SageMaker are the same as Azure ML. We can build our analytics on Jupyter and our training cluster through a Python API, and then deploy our MaaS that can be consumed through a REST API. SageMaker also supports foundational models to develop models on already existing pre-trained models.

SageMaker Studio is a full platform able to manage the entire model lifecycle development: from data management, model development, training or fine-tuning, experiment tracking, and delivering.

The basic steps to develop our analytical model with the Azure ML service are as follows:

1. Preparing the data
2. Developing the model with a rich tool, such as a Jupyter Notebook
...