---
title: "Public Cloud Learning Sessions (OpenText)- AI Use Cases - 20241126 160106-Meeting Recording"
type: cloud-learning
source-type: video
category: "DevOps & SRE/09_Serverless_AI"
tags:
  - AI
  - Use-Cases
  - OpenText
date-added: 2026-04-14
video-source: "nas:///volume2/work/Public Cloud Learning Sessions/Public Cloud Learning Sessions (OpenText)- AI Use Cases - 20241126_160106-Meeting Recording.mp4"
audio-source: ""
status: summarized (Gemini 摘要)
---

# Public Cloud Learning Sessions (OpenText)- AI Use Cases - 20241126 160106-Meeting Recording

**Source:** NAS `/volume2/work/Public Cloud Learning Sessions/Public Cloud Learning Sessions (OpenText)- AI Use Cases - 20241126_160106-Meeting Recording.mp4`

**Type:** VIDEO | **Category:** 09_Serverless_AI

**Status:** 🟡 Awaiting Whisper transcription → Summary

---

## AI Use Cases with AWS Experts

Stephen Frank, an AWS AI specialist, discusses AI innovation opportunities, leveraging data, and accelerating AI use cases. The session covers the evolution of AI, from its focus on mimicking human behavior to machine learning, deep learning, and the current Gen2 AI using large language models (LLMs).

Key factors driving the growth of Gen2 AI include the massive increase in data production since the 2000s and the availability of greater computational capacity. Cloud computing has enabled machine learning by providing the necessary resources. Enterprise software companies are early adopters of generative AI, integrating it into their core products for customer-facing applications.

Amazon has been using AI and machine learning in its core products and services for 25 years, applying its learnings to new offerings for customers. Common AI use cases include creating new customer experiences, extrapolating core insights from data, automating processes, and generating new content. For enterprise software, AI can optimize internal processes, enable new features, and create new offerings. *Data is key to differentiation, as generative AI applications integrate with existing business data to control outcomes.* Various methods exist for working with data, including retrieval-augmented generation (RAG), fine-tuning, and continued pre-training.

AWS offers a three-layered product strategy: infrastructure for foundation model training and inferences, Amazon Bedrock (a flagship product providing API access to various foundation models), and ready-to-use AI applications. Amazon SageMaker is a fully managed machine learning platform for data scientists and platform engineers. Amazon Bedrock allows access to various models without third-party access to data, ensuring GDPR compliance. Amazon Q is a pre-built AI system for knowledge summarization, content creation, and insight extraction, connecting to various data sources using natural language prompts.

Key considerations for AI implementation include fostering a culture of experimentation, ensuring flexibility in model selection, and prioritizing security, governance, and compliance. Responsible AI practices, including fairness, explainability, and transparency, are crucial. Best practices include prioritizing people, assessing risk, and iterating across the AI lifecycle. *When implementing your services, we do have to look at this more holistically.*