Intro to ML Pipelines and Experiment Tracking with Azure Machine Learning
Intro to ML Pipelines and Experiment Tracking with Azure Machine Learning
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 47m | 170 MB
Instructor: Deepak Goyal
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 47m | 170 MB
Instructor: Deepak Goyal
Learn to build ML pipelines and track experiments in Azure Machine Learning. This course will teach you to create structured workflows using Studio and SDK to organize, automate, and evaluate model runs at scale.
What you'll learn
As machine learning workflows grow more complex, managing experiments and scaling models efficiently becomes critical. In this course, Intro to ML Pipelines and Experiment Tracking with Azure Machine Learning, you’ll gain the ability to organize, automate, and evaluate ML workflows using the Azure ML platform.
First, you’ll explore the purpose of pipelines and build one using both the Studio visual designer and Python SDK. Next, you’ll discover how to submit pipeline jobs, monitor their execution, and troubleshoot logs and outputs. Finally, you’ll learn how Azure ML tracks experiments, logs, metrics, and outputs, as well as how to evaluate model performance based on those results.
When you’re finished with this course, you’ll have the skills and knowledge of Azure Machine Learning needed to build structured ML pipelines and effectively track experiments.
Intro to ML Pipelines and Experiment Tracking with Azure Machine Learning
