Learn Live: How to develop custom object models with data labeling tools and AutoML | CLL11

From the creator

Full series information: https://aka.ms/learnlive-at-build-22 More info here: https://aka.ms/learnlive-learn-live-at-build-22-Ep9 Follow on Microsoft Learn: - Microsoft Learn Module: https://aka.ms/learnlive-20220524I - Microsoft Learn Module (2): https://aka.ms/learnlive-20220524I2 Automated machine learning, or AutoML, is the process of automating the time-consuming, iterative tasks of machine learning model development. It allows data scientists, analysts, and developers to build ML models with high scale, efficiency, and productivity all while sustaining model quality. In this session, you’ll learn how to use Azure Machine Learning data labeling capabilities to create, manage, and monitor data labeling projects. Then, you’ll learn how to use AutoML to train your labeled dataset and develop a production object detection model. --------------------- Learning objectives - Create a Dataset of labeled images retrieved from an attached Datastore - Coordinate data, labels, and team members to efficiently manage and process labeling tasks - Track progress and maintain a queue of incomplete labeling tasks - Author AutoML models for vision tasks via the Azure ML Python SDK - Seamlessly integrate with the Azure Machine Learning data labeling capability - Optimize model performance by specifying the model algorithm and tuning the hyperparameters --------------------- Presenters Paul DeCarlo Principal Cloud Advocate Microsoft - Twitter: https://twitter.com/pjdecarlo - Linked

Published 2022-05-27 · Watch on YouTube ↗
Availability and playback restrictions are controlled by the original channel.