Introduction to MLOps using AzureML
AzureML consists of tools that make it easier to develop, train, deploy and monitor machine learning models within Azure. We will cover topics such as how to build an AzureML pipeline, build reusable components, use a model registry, and much more. At the end of this CodeBreakfast, you should know what AzureML is and how it can help you with your MLOps setup.
Jordi Smit Machine Learning Engineer GoDataDriven
Sander van Donkelaar Machine Learning Engineer Xebia
Introduction to MLOps using AWS SageMaker
We will guide you through how to build an end-to-end machine learning pipeline using AWS SageMaker. This will cover the preprocessing, training, evaluation and model deployment. SageMaker has many useful features to help building complex machine learning pipelines, in a simple way. We will begin with a simple pipeline that covers the main processes and then make this more robust by building out other components such as model evaluation, conditional deployment and scheduling.
Jeroen Overschie Machine Learning Engineer GoDataDriven
Usman Zafar Machine Learning Engineer GoDataDriven