Amazon SageMaker, Microsoft Azure ML Services, Google Cloud ML Engine, IBM Watson Studio are examples of ML PaaS in the cloud. If your business wants to bring agility into machine learning model development and deployment, consider ML PaaS. It combines the proven technique of CI/CD with ML model management.

This article is a post in a series on bringing continuous integration and deployment (CI/CD) practices to machine learning. Check back to The New Stack for future installments. For the background and context, we strongly recommend you to read the previous article on the rise of ML PaaS followed by the article on the overview of Azure ML service. In this tutorial, we will build and deploy a machine model to predict the salary from the Stackoverflow dataset. By the end of this, you will be able to invoke a RESTful web service to get the predictions.

Amazon SageMaker, Microsoft Azure ML Services, Google Cloud ML Engine, IBM Watson Knowledge Studio are examples of ML PaaS in the cloud. If your business wants to bring agility into machine learning model development and deployment, consider ML PaaS. It combines the proven technique of CI/CD with ML model management.

Amazon SageMaker, Microsoft Azure ML Services, Google Cloud ML Engine, IBM Watson Knowledge Studio are examples of ML PaaS in the cloud. If your business wants to bring agility into machine learning model development and deployment, consider ML PaaS. It combines the proven technique of CI/CD with ML model management.