In this course, you will learn to create high performance, deep learning applications easily with a streamlined development workflow. You will learn about running inference tools for low precision optimizations, computer vision libraries, media processing, and pre-optimized kernels. We will also teach you how to quickly deploy your AI applications productively across combinations of host processors and accelerators, including CPUs, GPUs, and VPUs, on-prem, on-device, and in the browser or cloud using the write once deploy anywhere approach. Throughout this course, you will learn how developers use the OpenVINO™ toolkit on multiple Intel® architectures to enable new and enhanced use cases across industries, including manufacturing, health and life sciences, retail, security, and more. This course with OpenVINO™ toolkit is focused on developing deep learning inference applications and not model training. OpenVINO™ toolkit provides a set of pre-trained models that you can use for learning and demo purposes or for developing deep learning software.

What You Will Learn :

  • Differentiate between AI, ML and DL, compare and select appropriate AI techniques, identify various components of DL applications, and identify features and components of the Intel® Distribution of OpenVINO™ toolkit and Intel® DevCloud.
  • Understand reasons to optimize and tune Deep Learning models for inference, use tools like Model Optimizer and POT, and make informed decisions for choosing the right optimization strategy.
  • Use Inference Engine workflow, implement SYNC and ASYNC execution modes, and run Inference Engine to deploy optimized and future ready AI Applications.
  • Evaluate different hardware platforms for AI inference, and differentiate/ select between various hardware platforms available in the Intel Ecosystem.
  • Work with various features of DL Workbench workflow, use various benchmarking tools present in the DL Workbench and quickly prototype DL application development using Jupyter* Notebooks on the Intel® DevCloud.

Who should attend ?

  • AI developers and researchers
  • AI enthusiasts
  • Data scientist
  • ML engineers

Prerequisites :

  • Experience with Python
  • Basic understanding of data processing and deep learning