Designing and Implementing a Data Science Solution on Azure (DP-100): Train and Deploy Models

Master Azure ML training, pipelines, and deployment aligned with the DP-100 exam. This course will teach you how to run jobs, manage models, and deploy solutions for production-ready machine learning.

Developing machine learning solutions in Azure can be a daunting task; from training models and managing pipelines to deploying endpoints at scale, numerous components must collaborate seamlessly. In this course, Designing and Implementing a Data Science Solution on Azure (DP-100): Train and Deploy Models, you’ll gain the ability to run, manage, and deploy models effectively using Azure Machine Learning, while preparing for key objectives of the DP-100 certification exam. First, you’ll explore how to run model training scripts, configure compute and environments, and track experiments with MLflow. Next, you’ll discover how to implement training pipelines by creating components, chaining steps, and automating recurring workflows. Finally, you’ll learn how to manage models with MLflow, deploy them to both online and batch endpoints, test deployed services, and troubleshoot deployments at scale. When you’re finished with this course, you’ll have the skills and knowledge of Azure Machine Learning model training, pipeline orchestration, and deployment needed to deliver production-ready machine learning solutions on Azure, and you’ll be well-prepared for the DP-100 certification exam.