Join us at a new APJ time for our monthly workshops on how you can optimize your AI applications.
Why join?
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 kick things off with a pre-recorded fire side chat with Yury Gorbachev, Intel Fellow, OpenVINO Product Architecture and Ray Lo, global lead of the Intel AI evangelist, discussing the biggest challenges and sharing rare insights from the past and present about the toolkit's evolution.
With this new release, we’ll highlight what’s new, including:
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.
Generative AI is exploding, bringing potential AI applications that could change everything we do. One example of this recent progress is the release of text processing 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 text processing, 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:
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:
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:
The integration of AI in laboratory environments is rapidly changing the way clinical pharmaceutical scientists extract meaningful insights and develop innovations that revolutionize healthcare. However, the optimization and deployment of scientific pipelines is often challenging due to laboratory settings and requirements. In this talk, we will demonstrate how to efficiently build and deploy scientific AI models in using open-source technologies. We will walk through an end-to-end case study, inviting Beckman Coulter Life Sciences to share how they leveraged OpenVINO optimizations and OpenVINO model server to unlock AI performance for their CellAI toolbox.
What you’ll learn:
OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference.
Get started: