Seeking AI resources for students in your university classroom?

A guide to a teacher kit, coding and dataset resources for AI inference.

Author: Jason Burris

It’s no secret that artificial intelligence (AI) is one of the hottest topics in the tech world today. Every day, it seems like there’s a new story about how AI is being used to improve some aspect of our lives, from personal assistants to driverless cars. Given all the hype, it’s no wonder that educators are eager to introduce AI concepts to their students. Now, thanks to resources inside Intel’s 5-module teaching kit for AI inference teaching the Intel® Distribution of OpenVINO™ toolkit, it is easier than ever to introduce the concepts of deep learning AI to students. Get your students hands-on coding experience with this teacher kit, which comes with a lesson plan, 5-modules of workbooks, videos, quizzes, and Jupyter* Notebook coding lab tutorials. The coding tutorials are focused on using Python* so it’s a good way to reinforce programming skills.

What Is AI Inference?

Inference is the process of using a trained machine or deep learning model to make predictions or decisions based on new data. In other words, it’s how we take the results of a deep learning training session and apply them to real-world problems. For example, you could use a trained deep learning model to automatically classify images, identify objects or text, convert speech to text, or process natural language. We use AI inference every day, in the search engines that answer our questions, in cameras that identify people, places and things, and in our home smart speakers which decipher our speech.

Why Teach AI Inference?

AI inference is a fundamental concept in deep learning, and it’s one that students will need to understand if they want to pursue careers in this growing field. AI inference is being incorporated into many types of industries from retail, healthcare, and manufacturing, to transportation, logistics, and more. Furthermore, inference is an ideal starting point for teaching AI because it doesn’t require extensive training data sets, complex math to build AI models, or access to high performance computing to train models — all of which can be difficult or expensive to obtain. The teacher kit is a great starting point because it focuses on applied AI; how to deploy AI locally and see results.

What is the OpenVINO™ toolkit?

The OpenVINO toolkit, short for Open Visual Inference and Neural Network Optimization, is a free, open-source toolkit for developing efficient deep learning (AI) inference applications. It includes optimized calls for different hardware types, including Intel® CPUs, GPUs, FPGAs, and VPUs (vision processing units). The magic of the toolkit is its write once, deploy anywhere API so that student developers can apply the AI in their application to multiple compute architectures in a variety of ways, even simultaneously. In addition, OpenVINO offers pre-trained models that can be quickly adapted to new tasks and offers a desktop analysis tool for checking the performance of models. Intel CPUs are ubiquitously available compute in PCs and laptops, and most include an integrated GPU too, providing a path for students to explore heterogeneous programming skills. The toolkit complements STEM courses that teach computer vision, natural language processing (NLP), and recommender system design using many popular AI framework libraries like TensorFlow* and PyTorch* being taught in CS and ECE departments.

What comes in the OpenVINO™ toolkit teacher kit?

The kit includes a lesson plan, workbooks, videos, lab tutorials and assessment quizzes. Additionally, Intel offers several complementary resources for skills building.

• A Github* repository of OpenVINO toolkit Jupyter Notebook tutorials. This is an easy path for students to install the OpenVINO toolkit and Jupyter Notebooks on their own laptop. They can learn how to do image classification, object detection, license plate recognition, or even have fun with AI using their own webcam.
• A Github repository of hundreds of pre-trained models provides access to their research papers in the Intel Open Model Zoo. These are great for student projects, and to dive as deep as they want into models’ published research concepts and math.
• An online cloud environment, Intel® Developer Cloud with OpenVINO preinstalled, is a resources students can code and deploy their AI enabled applications to a large variety of Intel based equipment. The Intel Developer Cloud also includes Jupyter Notebook tutorials. Access is free for 120-day increments and access extensions can be requested before expiration.

Conclusion:

If you’re looking for an easy way to introduce your students to the concepts of deep learning AI, look no further than Intel’s 5-module teaching kit for AI inference using the OpenVINO toolkit. This kit includes everything you need to get started, from a lesson plan and workbooks to videos, assessment quizzes, and lab tutorials. Request access to the Intel® Distribution of OpenVINO™ toolkit teacher kit today and start getting your students coding with AI! Access is available to confirmed educators. Lastly, consider visiting Intel’s Academic Resources for Edge and IoT webpage to find all these resources for educators and students.

Resources

1. Intel® Distribution of OpenVINO™ toolkit teacher kit
2. OpenVINO™ toolkit Jupyter* Notebook tutorials on Github*
3. Intel Open Model Zoo on Github*
4. Intel® Developer Cloud
5. Intel Academic Resources for Edge and IoT

Notices & Disclaimers

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