In the first half of the workshop, you will learn the most important concepts of the machine learning workflow that data scientists follow to build an end-to-end data science solutions on Azure. You will learn how to find, import, and prepare data; select a machine learning algorithm; train and test the model; deploy a complete model to an API. You will get tips, best practices, and resources you and your team need to continue your machine learning journey, build your first model, and more.
We will cover the following basic topics:
- What machine learning and when is machine learning the right tool
- How to select the right machine learning algorithm for your data science scenario on Azure
- How Azure Machine Learning tools will make your life easier
- Build a machine learning model with Azure Machine Learning designer
- Test, deploy and consume a machine learning model with Azure Machine Learning designer
In the second half of part 2 on advanced machine learning, we will introduce some challenges of deploying a machine learning model and we will discuss the following points in order to enable you to tackle some of those challenges:
- How to select the right tools to succeed with model deployment
- How model interpretability toolkits can be used for model training and deployment
- How to use Automated Machine Learning to optimize your machine learning deployment flow
- How to build multiple robust machine learning pipeline using tools such as Jupyter Notebooks, Virtual Machines and Containers
- How to register your model and transform it into a webservice that can be easily consumed by other data scientists and developers