Streamline AI for Data Generation and Large Language Models

Incorporating AI into an organization’s workloads or scaling up an already existing infrastructure is skill-heavy and computationally intensive, requiring the development of robust models trained on massive datasets and powerful GPUs on which to run them adequately.

Not every organization has the necessary resources to accomplish this task.

This session focuses on a solution: A collection of open source AI reference kits from Accenture* and Intel designed to make AI more accessible to organizations and optimized for improved training and inference time.

Specifically, the hour is dedicated to the kits that target data generation and large language models: text, image, and voice data generation.

Topics covered include:

• An introduction to these reference solutions, including how they address business-specific problems and speed up end-to-end AI pipelines with out-of-box optimizations.
• An overview of the kits designed for data generation and large language models.
• A demo that shows one or more of the kits in action.

Skill level: Intermediate

Each AI reference kit is built using AI Tools—familiar Python* tools and frameworks to accelerate end-to-end data science and analytics pipelines.

Download this session’s showcased reference kits from GitHub*:

• Text Data Generation for generating synthetic text, such as the provided source dataset, using a large language model (LLM).
• Image Data Generation for generating synthetic images using generative adversarial networks (GAN).
• Voice Data Generation for translating input text data to generate speech using transfer learning with vocoder models.