OpenVINO™ DevCon is Back!

Join us for monthly workshops on how you can optimize your AI applications. 

Why join?

  • Stay up-to-date with the latest trends in AI development
  • Put your knowledge into action with sample code applicable to your own solutions
  • Get direct insights from knowledgeable speakers

Register now   New to OpenVINO?

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OpenVINO™ 2023.0 – See What’s New


OpenVINO™ toolkit's newest release, 2023.0, marks a significant milestone as it celebrates its 5-year anniversary. Throughout its journey, OpenVINO has expanded its support for models from computer vision to natural language processing, deepening its frameworks integrations, and continuing to increase the portability and performance. The team has overcome new challenges while keeping an eye on the future. We’ll hear from the individual behind the product with rare insights from the past and present about the toolkit's evolution, Yury Gorbachev, Intel Fellow, OpenVINO Product Architecture. 

With this new release, we’ll highlight what’s new, including:

  • More integrations like TensorFlow and Pytorch Frontends
  • Expanded model support such as Segment Anything, GPT-J, and YOLOv8
  • Gaining efficiencies on CPU with thread scheduling

Lastly, we’ll cover the rapid advancements in language processing and what this means for the industry along with the impact on the open-source community. Can anyone really be future proof? Join us for a lively discussion and learn what’s new with OpenVINO.

Harness Generative AI Acceleration with OpenVINO™ toolkit

June 28 9AM-10AM PDT

The field of generative AI has been rapidly advancing, bringing with it potential applications that could fundamentally alter the future of human computer interactions and collaborations. One example of this recent progress is the release of GPT models, which possess the capability to solve complex problems like passing medical and law exams, akin to human abilities. However, one critical question remains: can we run these advanced models on CPUs, or the latest GPUs from Intel?

In this workshop, we'll delve into the world of transformer models, including Stable Diffusions and GPT, as well as explore how we've optimized these models to run on Intel’s wide variety of hardware. We'll also take a look at Jupyter Notebook tutorials that you can run on your own machine, providing you with hands-on experience with these powerful tools.

What you’ll learn:

  • AI applications can scale across GPUs and CPUs heterogeneously with Intel® Hardware
  • How to accelerate AI with Intel® hardware accelerators; Intel® Xe Matrix Extensions (Intel® XMX) and Intel® Advanced Matrix Extensions (Intel® AMX)
  • Deploying stable diffusion and GPT from sophisticated Jupyter Notebooks
  • Using Huggingface Transformers to create powerful AI solutions quickly
  • How dynamic shape optimization maximizes Deep Learning performance

Beyond the Continuum: The Importance of Quantization in Deep Learning

July 25 9AM-10AM PDT

Quantization is a valuable process in Deep Learning of mapping continuous values to a smaller set of discrete finite values. It is a powerful technique that can significantly reduce the memory footprint and computational requirements of deep learning models, making them more efficient and easier to deploy on resource-constrained devices.

In this talk, we will explore the different types of quantization techniques that can be applied to deep learning models. In addition, we will give an overview of the Neural Network Compression Framework (NNCF) and how it complements the OpenVINO™ Toolkit to achieve outstanding performance.

What you’ll learn:

  • The value of quantization and different types of quantization
  • How to harness NNCF with the OpenVINO™ toolkit
  • A Jupyter Notebook demonstrating a neural network graph before-and-after quantization with performance comparisons.

How To Build a Smart Queue Management System Step by Step? From Zero to Hero

August 22 9AM-10AM PDT

Join us for a step-by-step tutorial on how to create an intelligent retail queue management system using the OpenVINO™ toolkit and YOLOv8. We'll walk you through the process of integrating these powerful open-source tools to develop an end-to-end solution that can be deployed in retail checkout environments. Whether you're an experienced developer or new to AI, this session will provide practical tips and best practices for building intelligent systems using OpenVINO. By the end of the presentation, you'll have the knowledge and resources to build your own solution.

What you’ll learn: 

  • Step-by-step easy-to-follow Jupyter Notebook tutorial
  • Real-time detection and tracking of people for efficient queue management and staffing optimization
  • Optimized for multi-model workloads across various Intel processors
  • Where to find resources; open-source code, dataset, videos, and a blog available on GitHub for easy customization and extension to your specific needs

What is OpenVINO™ toolkit?

OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference.

  • Boost deep learning performance in computer vision, automatic speech recognition, natural language processing and other common tasks
  • Use models trained with popular frameworks like TensorFlow, PyTorch and more
  • Reduce resource demands and efficiently deploy on a range of Intel® platforms from edge to cloud

Get started:

  • Learn more about OpenVINO at
  • Download the latest Intel® Distribution of OpenVINO™ toolkit
  • Explore the OpenVINO™ toolkit Github repository; Jupyter Notebooks, Training Extensions, Models, and more…

Ready to Register?