Faster, Easier Machine Learning with OpenVINO™ and TensorFlow*

Implementing machine learning models can be daunting, particularly if you’re starting from scratch. But there’s good news: software innovations that work hand-in-glove can make the entire process faster and easier.

This video walks you through a prime example.

Follow Monique as she demonstrates, screen-by-screen, the process of using Model Optimizer (part of Intel® Distribution of OpenVINO™ toolkit) to prepare a TensorFlow* model for conversion, including using the toolkit’s samples to run inference and get accurate results. You’ll learn how to:

  • Create checkpoints, graph definitions, and event files
  •  Launch TensorBoard* to visualize your graph
  • Use OpenVINO with TensorFlow Object Detection API
  •  Convert a TensorFlow object with Model Optimizer and deploy it with an OpenVINO sample
  •  And more

All in under 15 minutes.

Be sure to download OpenVINO toolkit before watching so you can follow along on your own machine.

OpenVINO is a trademark of Intel Corporation or its subsidiaries in the U.S. and/or other countries.

Monique Jones, Technical Consulting Engineer, Intel Corporation

Monique is the technical lead for the OpenVINO™ toolkit on the U.S. team and supports Intel’s portfolio of visual computing products. Prior to her current role, she served the company as an automation software engineer, conducting multidisciplinary research and collaborating with hardware designers in Intel fabrication facilities. When she’s not developing software, Monique enjoys weight lifting, trying out new restaurants, and hiking. Monique earned a Bachelor of Science in Electrical Engineering/Computer Engineering from Texas State University.

For more complete information about compiler optimizations, see our Optimization Notice.