MLOps (also known as DevOps for machine learning) is the practice of collaboration and communication between data scientists and DevOps professionals to help manage the production machine learning (ML) lifecycle.

Azure Machine Learning service’s MLOps capabilities provide customers with asset management and orchestration services which enable effective ML lifecycle management.

Learn more about MLOps:
https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-model-management-and-deployment

Here’s part one of a series of blog posts that will explore the machine learning options available in Azure.

In this post series, I am going to show how we can use Azure Machine learning services and the new features added that make life so easy to train, deploy, automate managing machine learning models [1]. In this post, first I will show how to use a no code environment for Auto ML, how to access it and some difference between Azure mL Studio and services.

This video shows how to build, train and deploy a time series forecasting solution with Azure Machine Learning. You are guided through every step of the modeling process including:

  • Set up your development environment
  • Access and examine the data
  • Train using an Automated Machine Learning
  • Explore the results
  • Register and access your time series forecasting model through the Azure portal.