As shown in the following diagram, the first step in E2E machine learning is data preparation, which includes cleaning the data and featurization. Then, we have to create and train a machine learning model in the model training step. After that, we have model deployment, which means deploying the model as a web service to perform predictions. The final step is monitoring, which includes analyzing how the model is performing and then triggering the retraining of the model.
The Azure ML SDK enables professional data scientists and DevOps engineers to carry out E2E machine learning. It allows us to seamlessly use the power of the cloud to train and deploy our model. We can start using the Azure ML SDK easily by installing it using pip in any Python environment. We can scale the compute for training by using a cluster...
