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title: "Hands-on AIOps Best Practices Guide to Implementing AIOps"
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type: cloud-learning
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source-type: pdf
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category: "DevOps & SRE/09_Serverless_AI"
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tags:
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- AIOps
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- Best-Practices
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date-added: 2026-04-14
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video-source: "nas:///volume2/work/Public Cloud Learning Sessions/Hands-on AIOps Best Practices Guide to Implementing AIOps.pdf"
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audio-source: ""
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status: raw
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---
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# Hands-on AIOps Best Practices Guide to Implementing AIOps
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**Source:** NAS `/volume2/work/Public Cloud Learning Sessions/Hands-on AIOps Best Practices Guide to Implementing AIOps.pdf`
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**Type:** PDF | **Category:** 09_Serverless_AI
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**Status:** 🟡 Awaiting Whisper transcription → Summary
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---
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## 摘要
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> 待转录后由 LLM 生成
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---
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## 关键概念
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-
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---
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## 行动项
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-
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---
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## 相关视频
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> 配对视频笔记链接（生成后填入）
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---
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*最后更新: 2026-04-14*
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@@ -10,7 +10,7 @@ tags:
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date-added: 2026-04-14
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video-source: "nas:///volume2/work/Public Cloud Learning Sessions/Public Cloud Learning Sessions-Introduction to Artificial Intelligence (AI) Machine Learning (ML) - 20240206_160153-Meeting Recording.mp4"
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audio-source: ""
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status: raw
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status: summarized (Gemini 摘要)
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---
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# Public Cloud Learning Sessions-Introduction to Artificial Intelligence (AI) Machine Learning (ML) - 20240206 160153-Meeting Recording
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@@ -23,28 +23,20 @@ status: raw
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---
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## 摘要
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## Introduction to AI/ML with AWS
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> 待转录后由 LLM 生成
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The session introduces AI/ML, generative AI in AWS, and implementing the data science lifecycle in AWS, presented by Suraav Paul, AWS Senior Solutions Architect. It covers how AI/ML is transforming businesses and how Amazon and AWS are accelerating this transformation.
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---
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AI replicates tasks needing human intelligence, often seeking probabilistic outcomes via machine learning, which uses data to create decision logic or models. Classification AI identifies patterns, predictive AI forecasts trends, and generative AI creates content using foundation models (FMs). Amazon has invested in ML for 20 years, using it for recommendations, robotics, forecasting, and Alexa.
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## 关键概念
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AWS helps customers use AI in four areas: enhancing customer experiences, enabling better decisions, improving operations, and creating new products. The AI Use Case Explorer helps customers find relevant AI use cases. Responsible AI includes fairness, explainability, robustness, governance, transparency, and privacy/security. AWS offers pre-built algorithms, models, and solutions, democratizing access to AI/ML with tools like Amazon SageMaker Canvas.
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*We believe most customer experiences and applications will be reinvented with generative AI, powered by foundation models with billions of parameters.*
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---
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Amazon Bedrock is a fully managed service for building and scaling generative AI applications with foundation models, allowing customization with proprietary data while maintaining security and privacy. Bedrock offers access to various FMs, including Amazon Titan models, which provide capabilities, competitive pricing, and strong performance. Data customization techniques include fine-tuning (using labeled datasets) and continued pre-training (using unlabeled datasets). Retrieval augmented generation (RAG) fetches data from company sources for relevant responses. Agents for Amazon Bedrock plan and execute multi-step tasks using company systems and data sources. Guardrails for Amazon Bedrock enables safeguards tailored to application requirements and responsible AI policies.
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## 行动项
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ML Ops combines machine learning and operations, involving people, technology, and processes for collaborative ML solutions. It requires a diverse team and a culture that encourages collaboration. The ML Ops process includes data, training, and inference pipelines. The data pipeline involves data collection, integration, and preparation using services like Amazon S3 and Amazon Redshift. The training pipeline focuses on feature engineering, model training, and hyperparameter tuning using SageMaker. The inference pipeline deploys and monitors models using SageMaker's real-time endpoint. ML Ops addresses concerns around data provenance, model management, and deployment workflows, in addition to DevOps practices like CI/CD and monitoring.
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-
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During the Q&A, it was clarified that training data used from a company won't be used generally by the model. For first-party models, no additional licensing is needed, but third-party models have their own licensing agreements. Bedrock stores data only for the request-response cycle, ensuring data privacy. Previously trained models can be imported to SageMaker, while Bedrock offers a managed environment without needing to manage deployment infrastructure.
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---
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## 相关视频
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> 配对视频笔记链接（生成后填入）
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---
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*最后更新: 2026-04-14*
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*AI is a way to describe any system that can replicate tasks that previously required human intelligence.*
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@@ -0,0 +1,50 @@
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---
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title: "Public Cloud Learning Sessions-Introduction to Artificial Intelligence (AI) Machine Learning (ML) - 20240206 160153-Meeting Recording"
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type: cloud-learning
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||||
source-type: video
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||||
category: "DevOps & SRE/09_Serverless_AI"
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tags:
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- AI
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- ML
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- Machine-Learning
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date-added: 2026-04-14
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video-source: "nas:///volume2/work/Public Cloud Learning Sessions/Public Cloud Learning Sessions-Introduction to Artificial Intelligence (AI) Machine Learning (ML) - 20240206_160153-Meeting Recording.mp4"
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audio-source: ""
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status: raw
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---
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# Public Cloud Learning Sessions-Introduction to Artificial Intelligence (AI) Machine Learning (ML) - 20240206 160153-Meeting Recording
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||||
**Source:** NAS `/volume2/work/Public Cloud Learning Sessions/Public Cloud Learning Sessions-Introduction to Artificial Intelligence (AI) Machine Learning (ML) - 20240206_160153-Meeting Recording.mp4`
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**Type:** VIDEO | **Category:** 09_Serverless_AI
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||||
**Status:** 🟡 Awaiting Whisper transcription → Summary
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||||
|
||||
---
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||||
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||||
## 摘要
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||||
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||||
> 待转录后由 LLM 生成
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---
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## 关键概念
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||||
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-
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---
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## 行动项
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-
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---
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## 相关视频
|
||||
|
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> 配对视频笔记链接（生成后填入）
|
||||
|
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---
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||||
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||||
*最后更新: 2026-04-14*
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@@ -10,7 +10,7 @@ tags:
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||||
date-added: 2026-04-14
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video-source: "nas:///volume2/work/Public Cloud Learning Sessions/Public Cloud Learning Sessions (OpenText)- AI Use Cases - 20241126_160106-Meeting Recording.mp4"
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audio-source: ""
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status: raw
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||||
status: summarized (Gemini 摘要)
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---
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# Public Cloud Learning Sessions (OpenText)- AI Use Cases - 20241126 160106-Meeting Recording
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@@ -23,28 +23,14 @@ status: raw
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---
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## 摘要
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## AI Use Cases with AWS Experts
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> 待转录后由 LLM 生成
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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).
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---
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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.
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## 关键概念
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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.
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-
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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.
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---
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## 行动项
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||||
-
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---
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||||
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## 相关视频
|
||||
|
||||
> 配对视频笔记链接（生成后填入）
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---
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*最后更新: 2026-04-14*
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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.*
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@@ -1,24 +1,23 @@
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---
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||||
title: "Public Cloud Learning Sessions (OpenText)- AI Use Cases - 20241126 160106-Presentation"
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||||
title: "Public Cloud Learning Sessions (OpenText)- AI Use Cases - 20241126 160106-Meeting Recording"
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||||
type: cloud-learning
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||||
source-type: pdf
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||||
source-type: video
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||||
category: "DevOps & SRE/09_Serverless_AI"
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||||
tags:
|
||||
- AI
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- Use-Cases
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- OpenText
|
||||
- Presentation
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||||
date-added: 2026-04-14
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||||
video-source: "nas:///volume2/work/Public Cloud Learning Sessions/Public Cloud Learning Sessions (OpenText)- AI Use Cases - 20241126_160106-Presentation.pdf"
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video-source: "nas:///volume2/work/Public Cloud Learning Sessions/Public Cloud Learning Sessions (OpenText)- AI Use Cases - 20241126_160106-Meeting Recording.mp4"
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audio-source: ""
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||||
status: raw
|
||||
---
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||||
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||||
# Public Cloud Learning Sessions (OpenText)- AI Use Cases - 20241126 160106-Presentation
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||||
# 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-Presentation.pdf`
|
||||
**Source:** NAS `/volume2/work/Public Cloud Learning Sessions/Public Cloud Learning Sessions (OpenText)- AI Use Cases - 20241126_160106-Meeting Recording.mp4`
|
||||
|
||||
**Type:** PDF | **Category:** 09_Serverless_AI
|
||||
**Type:** VIDEO | **Category:** 09_Serverless_AI
|
||||
|
||||
**Status:** 🟡 Awaiting Whisper transcription → Summary
|
||||
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||||
@@ -20,7 +20,7 @@ status: processed
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||||
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**Type:** VIDEO | **Category:** 09_Serverless_AI
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**Status:** 🟢 Processed
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||||
**Status:** ✅ 已完成（Gemini 摘要）
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---
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@@ -11,7 +11,7 @@ tags:
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||||
date-added: 2026-04-14
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video-source: "nas:///volume2/work/Public Cloud Learning Sessions/Public Cloud Learning Sessions (OpenText)- Event Driven Architecture - part 2 - 20240917_161635-Meeting Recording.mp4"
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audio-source: ""
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||||
status: raw
|
||||
status: summarized (Gemini 摘要)
|
||||
---
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||||
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||||
# Public Cloud Learning Sessions (OpenText)- Event Driven Architecture - part 2 - 20240917 161635-Meeting Recording
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@@ -24,28 +24,28 @@ status: raw
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||||
---
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||||
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## 摘要
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## Event-Driven Architecture Best Practices
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> 待转录后由 LLM 生成
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Event-driven architecture helps decouple applications, allowing for logical decomposition of business functionality. It enables process isolation, which can be scaled and monitored independently, minimizing the impact of failures in one part of the system on the rest. *Event is nothing but it's like a change in the state or an update*.
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---
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In event-driven architecture, there are three parts: event producer, event consumer, and event broker. Event brokers can be event routers (SNS, EventBridge) or event stores (SQS, Kinesis). Event routers filter events and route them to the right consumer, while event stores stream events and require consumers to filter the events they want. EventBridge is more feature-rich than SNS, allowing events from a source product to trigger other AWS services.
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## 关键概念
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Choreography involves different microservices communicating with each other, while orchestration happens within the same microservice. AWS Step Functions is a workflow service that builds state machines, where each step is a state and transitions move from one state to the next.
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-
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### Best Practices for Event-Driven Architecture
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---
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When designing systems, especially microservices, it's important to consider best practices for event-driven architecture. Events can be sparse (minimal information) or full state descriptions (many details). Sparse events are small and great for frequently changing data, but may require consumers to retrieve more details, potentially overwhelming services. Full state descriptions include more detail, but may be limited by EventBridge payload sizes.
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## 行动项
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Idempotency ensures that executing the same request multiple times yields the same result. Services processing events should follow idempotency to avoid unintended side effects. AWS Lambda automatically retries asynchronous invocations, so idempotency is crucial for managing orders and payments.
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To increase scale and resiliency, an event store like SQS can buffer events between microservices. SQS holds messages until services are available to process them. For unordered events, EventBridge or standard SQS queues can be used, but applications must handle out-of-order messages. To preserve event ordering, SQS FIFO or Kinesis Data Streams can be used.
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---
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### Team Independence
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## 相关视频
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When implementing event-driven architectures, it's important to consider team independence. Platform teams create the foundational layer, while consumer teams use events for various purposes. Decentralized ownership is generally preferred over centralized ownership. Fan-out patterns using SNS topics or EventBridge rules can distribute events to different teams.
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> 配对视频笔记链接（生成后填入）
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Best practices include a cloud center of excellence, decentralized team ownership, centralized networking, security, and observability strategies. Common messaging patterns include the competing consumer pattern, where only one consumer can consume a message at a time (achieved with SQS). Hybrid deliveries use EventBridge rules to route messages to different microservices.
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---
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### Common Messaging Patterns
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*最后更新: 2026-04-14*
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Streams and routers involve choreographing or orchestrating services based on event rules. EventBridge can route requests to specific microservices based on rules. Best practices for EventBridge include using single rule per subscriber, avoiding the default event bus for custom events, and using dead-letter queues to handle failed events. *Everything fails every time means like whatever you have designed and whatever workload you are running it may fail any time*.
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@@ -1,23 +1,24 @@
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||||
---
|
||||
title: "Public Cloud Learning Sessions (OpenText)- Event Driven Architecture - 20240917 161635 - presentation"
|
||||
title: "Public Cloud Learning Sessions (OpenText)- Event Driven Architecture - part 2 - 20240917 161635-Meeting Recording"
|
||||
type: cloud-learning
|
||||
source-type: pdf
|
||||
source-type: video
|
||||
category: "DevOps & SRE/09_Serverless_AI"
|
||||
tags:
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- EDA
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- Event-Driven
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||||
- Architecture
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- OpenText
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||||
date-added: 2026-04-14
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||||
video-source: "nas:///volume2/work/Public Cloud Learning Sessions/Public Cloud Learning Sessions (OpenText)- Event Driven Architecture - 20240917_161635 - presentation.pdf"
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||||
video-source: "nas:///volume2/work/Public Cloud Learning Sessions/Public Cloud Learning Sessions (OpenText)- Event Driven Architecture - part 2 - 20240917_161635-Meeting Recording.mp4"
|
||||
audio-source: ""
|
||||
status: raw
|
||||
---
|
||||
|
||||
# Public Cloud Learning Sessions (OpenText)- Event Driven Architecture - 20240917 161635 - presentation
|
||||
# Public Cloud Learning Sessions (OpenText)- Event Driven Architecture - part 2 - 20240917 161635-Meeting Recording
|
||||
|
||||
**Source:** NAS `/volume2/work/Public Cloud Learning Sessions/Public Cloud Learning Sessions (OpenText)- Event Driven Architecture - 20240917_161635 - presentation.pdf`
|
||||
**Source:** NAS `/volume2/work/Public Cloud Learning Sessions/Public Cloud Learning Sessions (OpenText)- Event Driven Architecture - part 2 - 20240917_161635-Meeting Recording.mp4`
|
||||
|
||||
**Type:** PDF | **Category:** 09_Serverless_AI
|
||||
**Type:** VIDEO | **Category:** 09_Serverless_AI
|
||||
|
||||
**Status:** 🟡 Awaiting Whisper transcription → Summary
|
||||
|
||||
@@ -10,7 +10,7 @@ tags:
|
||||
date-added: 2026-04-14
|
||||
video-source: "nas:///volume2/work/Public Cloud Learning Sessions/Public Cloud Learning Sessions (OpenText)- Generative AI & Prompt Engineering - 20241112_160112-Meeting Recording.mp4"
|
||||
audio-source: ""
|
||||
status: raw
|
||||
status: summarized (Gemini 摘要)
|
||||
---
|
||||
|
||||
# Public Cloud Learning Sessions (OpenText)- Generative AI & Prompt Engineering - 20241112 160112-Meeting Recording
|
||||
@@ -23,28 +23,30 @@ status: raw
|
||||
|
||||
---
|
||||
|
||||
## 摘要
|
||||
## Generative A.I. and Front-Engineering
|
||||
|
||||
> 待转录后由 LLM 生成
|
||||
The learning session covers Generative A.I. on AWS, common use cases, AWS services like Amazon Q, Amazon Better or Amazon SageMaker, and the basics of Front-Engineering. Basic familiarity with Generative A.I. concepts and terminology is required.
|
||||
|
||||
---
|
||||
Shikad Holtzman, a technical account manager based in Israel, discusses innovation opportunities, common use cases across industries, and how to make Generative AI applications valuable for business using data. The presentation includes AWS services and prompt engineering concepts.
|
||||
|
||||
## 关键概念
|
||||
Generative AI can create value by creating new experiences, boosting employee productivity, extracting insights, and fostering creativity. Use cases span customer experience (chatbots, virtual assistants), employee activities (code generation, summarization), business operations (document processing), and creative tasks (image generation). Amazon uses AI/ML, including Generative AI, for innovation, such as summarizing customer reviews on product pages.
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||||
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||||
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||||
*Your data is your differentiator and it is what makes the difference between generic application to a specific application that can actually bring business to your value, to your business, sorry.*
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||||
|
||||
---
|
||||
To create specific generative applications, techniques include retrieval augmented generation, fine-tuning, and continued retraining. Retrieval augmented generation is the cheapest and easiest, connecting multiple data sources without retraining the model. Fine-tuning involves retraining the model with labeled examples. Continued retraining adapts the model to a specific domain using unlabeled data.
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||||
## 行动项
|
||||
AWS allows users to move quickly, use their own data, and scale using its global infrastructure. The AWS Generative AI stack has three layers: infrastructure, services (Amazon Bedrock), and applications.
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||||
Amazon SageMaker is a managed service for the entire life cycle of building, training, and deploying foundation models. SageMaker Jumpstart provides access to publicly available foundation models and third-party models. AWS also offers dedicated chips like AWS trainium and AWS inferencia for training and inference.
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||||
|
||||
---
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||||
Amazon Bedrock is a fully managed service providing access to a wide range of foundation models from Anthropic, Meta, and Amazon (Titan models), including multi-modal and image models. Bedrock includes customization options, a managed RAG solution (knowledge bases), fine-tuning, continued training, agents, and responsible AI capabilities.
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||||
|
||||
## 相关视频
|
||||
*None of your data nor not the prompts, not the data that you are using for customizing the model is being shared with the model providers.*
|
||||
|
||||
> 配对视频笔记链接（生成后填入）
|
||||
Guardrails for Amazon Bedrock allow users to filter harmful content based on their own policies.
|
||||
|
||||
---
|
||||
Amazon Q is an AI-powered assistant with flavors like Amazon Q for business and Amazon Q developer. Amazon Q for business connects to multiple data sources for search, summarization, and insight extraction, maintaining existing permissions. Amazon Q for developer focuses on code generation, unit testing, and code migration.
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||||
|
||||
*最后更新: 2026-04-14*
|
||||
Prompt engineering involves creating, designing, and optimizing prompts to guide the LLM's response, ensuring accuracy and relevancy. LLMs are trained on data created by humans, so prompts should consider human responses. The process is iterative, involving testing and refining prompts against use cases. Instructions should be clear, accurate, and specific.
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||||
|
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Components of a prompt include instructions, context, user input, and an output indicator. Basic techniques include one-shot prompting and few-shot prompting, where examples are provided to the model. Chain of thoughts involves providing the model with step-by-step thinking to solve complex tasks.
|
||||
|
||||
@@ -0,0 +1,50 @@
|
||||
---
|
||||
title: "Public Cloud Learning Sessions (OpenText)- Generative AI & Prompt Engineering - 20241112 160112-Meeting Recording"
|
||||
type: cloud-learning
|
||||
source-type: video
|
||||
category: "DevOps & SRE/09_Serverless_AI"
|
||||
tags:
|
||||
- Generative-AI
|
||||
- Prompt-Engineering
|
||||
- OpenText
|
||||
date-added: 2026-04-14
|
||||
video-source: "nas:///volume2/work/Public Cloud Learning Sessions/Public Cloud Learning Sessions (OpenText)- Generative AI & Prompt Engineering - 20241112_160112-Meeting Recording.mp4"
|
||||
audio-source: ""
|
||||
status: raw
|
||||
---
|
||||
|
||||
# Public Cloud Learning Sessions (OpenText)- Generative AI & Prompt Engineering - 20241112 160112-Meeting Recording
|
||||
|
||||
**Source:** NAS `/volume2/work/Public Cloud Learning Sessions/Public Cloud Learning Sessions (OpenText)- Generative AI & Prompt Engineering - 20241112_160112-Meeting Recording.mp4`
|
||||
|
||||
**Type:** VIDEO | **Category:** 09_Serverless_AI
|
||||
|
||||
**Status:** 🟡 Awaiting Whisper transcription → Summary
|
||||
|
||||
---
|
||||
|
||||
## 摘要
|
||||
|
||||
> 待转录后由 LLM 生成
|
||||
|
||||
---
|
||||
|
||||
## 关键概念
|
||||
|
||||
-
|
||||
|
||||
---
|
||||
|
||||
## 行动项
|
||||
|
||||
-
|
||||
|
||||
---
|
||||
|
||||
## 相关视频
|
||||
|
||||
> 配对视频笔记链接（生成后填入）
|
||||
|
||||
---
|
||||
|
||||
*最后更新: 2026-04-14*
|
||||
@@ -11,7 +11,7 @@ tags:
|
||||
date-added: 2026-04-14
|
||||
video-source: "nas:///volume2/work/Public Cloud Learning Sessions/Public Cloud Learning Sessions (OpenText)- Serverless Computing - 20240903_160139-Meeting Recording.mp4"
|
||||
audio-source: ""
|
||||
status: raw
|
||||
status: summarized (Gemini 摘要)
|
||||
---
|
||||
|
||||
# Public Cloud Learning Sessions (OpenText)- Serverless Computing - 20240903 160139-Meeting Recording
|
||||
@@ -24,28 +24,14 @@ status: raw
|
||||
|
||||
---
|
||||
|
||||
## 摘要
|
||||
## Serverless Computing on AWS
|
||||
|
||||
> 待转录后由 LLM 生成
|
||||
This session covers serverless computing with a focus on AWS Lambda, step functions, and API Gateway. Modern businesses face pressure to innovate quickly, maintain security and compliance, respond to events, and increase profitability. Serverless computing simplifies cloud application management by shifting operational tasks to the cloud provider, allowing development teams to focus on code.
|
||||
|
||||
---
|
||||
Customers adopt serverless models for faster time to market, business focus, lower TCO, pay-per-use, scalability, and built-in security. AWS offers a range of serverless services, including Lambda, Fargate, and EventBridge. AWS and the customer share operational responsibilities. AWS manages infrastructure in serverless environments, while customers manage code.
|
||||
|
||||
## 关键概念
|
||||
AWS compute offerings include EC2, Fargate, and Lambda. EC2 offers flexibility and control, while Lambda allows developers to focus on business logic. *Lambda functions are triggered by events, which are changes in state.* Lambda handles load balancing, auto scaling, and security. Lambda functions can be triggered synchronously, asynchronously, or via event source mapping. When writing Lambda functions, developers need to consider the handler, event object, and context.
|
||||
|
||||
-
|
||||
Lambda permissions include execution roles (what the Lambda function can do) and resource-based policies (who can trigger the Lambda function). AWS Lambda includes a dashboard and metrics reported to CloudWatch, such as requests, errors, latency, and throttling. Amazon Q can be used to debug Lambda functions. Test events can be created to test Lambda functions. Versioning and aliases are important for managing code changes. *Whenever you see that you have written code and you want that this code is final, you can publish as a new version.* Lambda layers allow sharing common code across multiple Lambda functions. Lambda supports both x86 and ARM64 architectures. ARM64 offers better price performance. Lambda power tuning can be used to compare performance and cost.
|
||||
|
||||
---
|
||||
|
||||
## 行动项
|
||||
|
||||
-
|
||||
|
||||
---
|
||||
|
||||
## 相关视频
|
||||
|
||||
> 配对视频笔记链接（生成后填入）
|
||||
|
||||
---
|
||||
|
||||
*最后更新: 2026-04-14*
|
||||
Step functions orchestrate multiple AWS services. Step functions are serverless workflow services based on state machines. Step functions have two flavors: standard and express. API Gateway is a managed service for creating, publishing, and securing APIs. API Gateway offers edge-optimized, regional, and private options. The Serverless Application Model (SAM) is a tool for local development and deployment of serverless applications. SAM is built on top of CloudFormation. SAM local can be used to test Lambda functions locally.
|
||||
|
||||
@@ -0,0 +1,51 @@
|
||||
---
|
||||
title: "Public Cloud Learning Sessions (OpenText)- Serverless Computing - 20240903 160139-Meeting Recording"
|
||||
type: cloud-learning
|
||||
source-type: video
|
||||
category: "DevOps & SRE/09_Serverless_AI"
|
||||
tags:
|
||||
- Serverless
|
||||
- AWS
|
||||
- Lambda
|
||||
- OpenText
|
||||
date-added: 2026-04-14
|
||||
video-source: "nas:///volume2/work/Public Cloud Learning Sessions/Public Cloud Learning Sessions (OpenText)- Serverless Computing - 20240903_160139-Meeting Recording.mp4"
|
||||
audio-source: ""
|
||||
status: raw
|
||||
---
|
||||
|
||||
# Public Cloud Learning Sessions (OpenText)- Serverless Computing - 20240903 160139-Meeting Recording
|
||||
|
||||
**Source:** NAS `/volume2/work/Public Cloud Learning Sessions/Public Cloud Learning Sessions (OpenText)- Serverless Computing - 20240903_160139-Meeting Recording.mp4`
|
||||
|
||||
**Type:** VIDEO | **Category:** 09_Serverless_AI
|
||||
|
||||
**Status:** 🟡 Awaiting Whisper transcription → Summary
|
||||
|
||||
---
|
||||
|
||||
## 摘要
|
||||
|
||||
> 待转录后由 LLM 生成
|
||||
|
||||
---
|
||||
|
||||
## 关键概念
|
||||
|
||||
-
|
||||
|
||||
---
|
||||
|
||||
## 行动项
|
||||
|
||||
-
|
||||
|
||||
---
|
||||
|
||||
## 相关视频
|
||||
|
||||
> 配对视频笔记链接（生成后填入）
|
||||
|
||||
---
|
||||
|
||||
*最后更新: 2026-04-14*
|
||||
Reference in New Issue
Block a user