In this session you will learn how to maximize the performance of machine learning algorithms on Intel® Architecture with optimization libraries for popular Python frameworks. Get practical insights and explore real-world use cases during demo for achieving exceptional speedup in classical ML applications with only few lines of code.

During this presentation, we'll showcase the most recent advancements in Generative AI, covering Large Language Models and Diffusion Models. We will explore how Intel plays a crucial role in powering this technology, from training and fine-tuning to inference across a spectrum of Intel hardware platforms.

Intel Developer Cloud is a development environment that gives access to cutting-edge Intel hardware and software innovations to build and test AI, machine learning, HPC, and security applications for cloud, enterprise, client, and edge deployments. Learn about Intel's advanced CPUs, GPUs, and accelerators, along with open software tools, to optimize your AI products and solutions. Discovery session with vouchers for all participants

Intel Geti is a software platform that facilitates the creation of computer vision models in a fraction of the time and with minimal data. It streamlines laborious tasks such as data labeling, model training, and optimization throughout the AI model development process, empowering teams to generate custom AI models at scale. In this presentation, we will explore diverse computer vision use cases in different industries, showcasing how Intel Geti simplifies the process of model training,…

Participants will gain insights into how Intel's AI optimizations are contributing to eco-friendly solutions, reducing carbon footprints, and promoting sustainable practices in the tech industry. In the demo part, we will measure the carbon footprint from AI model and check how we could reduce the carbon footprint for 1 year inference. 

Standard machine learning approaches require centralizing the training data on one machine or in a datacenter. Federated Learning enables training models on distributed and private datasets without the need to centralize them. Privacy and security are key considerations for data set owners participating in Federated Learning optimizations.

Your code is making you lose money and increasing CO2 emissions. Learn how to harness LLMs and ML-based optimisation technologies to enhance software performance, reduce costs, and cut carbon emissions

Extended Abstract:

In today’s digital landscape, efficiency is more than a luxury—it’s a necessity. Businesses worldwide are striving to streamline operations and reduce costs, yet they often overlook the importance of optimising their code. Inefficient code can be a silent drain on resources,…

Local-first is a new architecture for building fast, collaborative and resilient software applications. This session will cover the benefits and trade-offs with local-first architecture and walk through the practical steps of creating a local-first application with conflict-free, active-active replication between Postgres in the cloud and SQLite in the local device. electric-sql.com