A clean energy platform that connects residents, businesses, and other consumers with clean energy facilities that lower their electric costs, centralized data from CRM and billing systems in BigQuery for churn analysis. Historically, account managers manually reviewed cancellation cases, parsing emails and call transcripts to categorize reasons using a complex 65-category system, later condensed into 16 actionable insights. By leveraging Google Gemini, we automated this process, training the LLM to analyze customer interactions and assign accurate categories, streamlining operations and enhancing retention strategies.

Generative AI is under the spotlight and it has diverse applications but there are also many considerations when deploying a generative model at scale. This presentation will make a deep dive into multiple architectures and talk about optimization hacks for the sophisticated data pipelines that generative AI requires. The session will cover: - How to create and prepare a dataset for training at scale in single GPU and multi GPU environments. - How to optimize your data pipeline for training and inference in production considering the complex deep learning models that need to be run. - Tradeoff between higher quality outputs versus training time and resources and processing times.

Agenda: - Basic concepts in Generative AI: GAN networks and Stable Diffusion - Training and inference data pipelines - Industry applications and use cases

Talk by: Paula Martinez and Rodrigo Beceiro

Here’s more to explore: LLM Compact Guide: https://dbricks.co/43WuQyb Big Book of MLOps: https://dbricks.co/3r0Pqiz

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