This course is designed for application developers who wants to deploy computer vision inference workloads using the Intel® Distribution of OpenVINOTM toolkit. The course looks at computer vision neural network models from a variety of popular machine learning frameworks and covers writing a portable application capable of deploying inference on a range of compute devices.
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The course is targeted for application developers, and places focus on examples and discussion of the development workflow. As such, the discussions include not only the details about how to use the toolkit itself, but topics like how to take benchmarks to compare compute devices or what to do when you encounter issues. The course is made so that it serves as a how-to guide for developing a computer vision inference deployment with the toolkit. By the end of the course, students will have the skillset necessary to deploy their own computer vision application using the toolkit.
What You Will Learn
- How to use the Model Optimizer to convert models from popular machine learning frameworks.
- How to run benchmarks and do device comparison and validation.
- How to use the Inference Engine to deploy to multiple types of devices.
- How to use the Inference Engine’s asynchronous inference to handle various deployment situations.
Syllabus
WEEK 1: Intermediate Intel® Distribution of OpenVINO™ toolkit for Deep Learning Applications
