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Modeling Audience Insights – How Act-On Used AI to Find More Customers

Wednesday, April 24, 2024 | 9:00 AM PT

Find out how Act-On leveraged Intel’s AI software portfolio of ready-to-use reference kits to optimally uncover the right customers at the right time.

 

Building performant and scalable AI applications to address specific industry needs requires AI and data science skills, domain expertise, and a bevy of tools, libraries, and optimization methods. It can be complicated. Worse, it can slow down the time-to-solution.

This session introduces a remedy: modular, domain-specific AI reference kits from Intel. Plus, it illustrates the power of these kits with a real-world solution that Act-On Software, in collaboration with Intel, built by using them.

Key takeaways:

  1. An overview of 5 domain-specific AI reference kits, including how to run them with Docker, Argo workflows on Kubernetes using Helm, and bare metal.
  2. A case study with industry partner Act-On, who used Intel-optimized AI ref kits and tools to build an efficient, scalable customer-segmentation solution at great time and cost savings.
  3. How to easily customize the kits to meet your specific use case.

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Skill level: Novice

Featured software

This session features the following 5 AI reference solutions:


 
Sudhir Kulkarni
VP of Data & AI Platforms, Intel
Syed Ahmed
SVP of Engineering, Act-On Software

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LLM Optimizations using Transformers and PyTorch on CPU and GPU

Wednesday, May 8, 2024 | 9:00 AM PT

Learn how to significantly enhance LLM performance on Intel platforms by leveraging the features of two powerful Intel-optimized libraries.

 

Large language models and the applications built around them have emerged as powerful tools for understanding and generating natural language. However, optimizing for these models for maximum efficiency and performance remains a significant challenge.

This session introduces a solution: How to optimize LLM workloads on target hardware using the Intel extensions for transformers and PyTorch.

Key takeaways:

  1. Introduction to Intel® Extension for Transformers and Intel® Extension for PyTorch, two powerful libraries for enhancing AI workload performance on Intel platforms.
  2. Using the PyTorch extension’s API calls to optimize LLM performance and memory usage.
  3. Using the transformers extension’s optimization features such as model compression, neural speed, and neural chat, which is a framework to build customized chatbots.

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Skill level: Novice

Featured software

Download the following, per your preference:


Download code samples


 
Kevin Ta
AI Software Solutions Engineer, Intel
Pramod Pai
AI Software Solutions Engineer, Intel

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