狠狠撸s for the presentation at Elastic {ON} Tour Tokyo 2017
https://www.elastic.co/elasticon/tour/2017/tokyo
Session Video: https://www.elastic.co/jp/elasticon/tour/2017/tokyo/microsoft
Web App for Containers + Cosmos DBで コンテナ対応したMEANアプリを作ろう!Yoichi Kawasaki
?
狠狠撸s for Azure Webinar: Containerized MEAN App on Azure PaaS
Web App for Containers は、アプリスタックのホストに Docker コンテナーを使用するため皆さんが今Linux上で利用しているOSSベースのアプリもアプリスタックごとDockerコンテナ化することでそのまま?Web App for Containersで利用することができます。
本ウェビナーでは簡単なMEANスタックアプリを題材に、アプリをコンテナ化し Web App for Containersにデプロイするまでの一連の流れを解説します。
MEANスタックのMongoDB部分についてはAzure Cosmos DBのMongo APIを利用して完全マネージドな構成を実現します。
Windows Server Container and Windows Subsystem for LinuxTakeshi Fukuhara
?
Windows Server コンテナーと Windows Subsystem for Linuxについて説明したAzureウェビナー資料。Windows Server 2019ベースで紹介。2018年11月27日に実施したWebinarの資料に、Azure Container Registryのスライドを追加したもの。
Azure Data Box Family Overview and Microsoft Intelligent Edge StrategyTakeshi Fukuhara
?
2019年2月26日に実施した "Azure を利用したインフラのモダナイズ!Azure File Sync と Azure Data Box 特集セミナー" でのセッション資料。Azure Data Boxファミリー概要と、マイクロソフトのインテリジェントエッジ戦略におけるAzure Data Box Ege/Gatewayの位置づけについての説明。Appendixには、Azure StackとAzure Data Box Edgeの比較スライドあり。
Web App for Containers + Cosmos DBで コンテナ対応したMEANアプリを作ろう!Yoichi Kawasaki
?
狠狠撸s for Azure Webinar: Containerized MEAN App on Azure PaaS
Web App for Containers は、アプリスタックのホストに Docker コンテナーを使用するため皆さんが今Linux上で利用しているOSSベースのアプリもアプリスタックごとDockerコンテナ化することでそのまま?Web App for Containersで利用することができます。
本ウェビナーでは簡単なMEANスタックアプリを題材に、アプリをコンテナ化し Web App for Containersにデプロイするまでの一連の流れを解説します。
MEANスタックのMongoDB部分についてはAzure Cosmos DBのMongo APIを利用して完全マネージドな構成を実現します。
Windows Server Container and Windows Subsystem for LinuxTakeshi Fukuhara
?
Windows Server コンテナーと Windows Subsystem for Linuxについて説明したAzureウェビナー資料。Windows Server 2019ベースで紹介。2018年11月27日に実施したWebinarの資料に、Azure Container Registryのスライドを追加したもの。
Azure Data Box Family Overview and Microsoft Intelligent Edge StrategyTakeshi Fukuhara
?
2019年2月26日に実施した "Azure を利用したインフラのモダナイズ!Azure File Sync と Azure Data Box 特集セミナー" でのセッション資料。Azure Data Boxファミリー概要と、マイクロソフトのインテリジェントエッジ戦略におけるAzure Data Box Ege/Gatewayの位置づけについての説明。Appendixには、Azure StackとAzure Data Box Edgeの比較スライドあり。
2015年12月7日に開催されたIVS CTO Night & Day 2015 WinterのSession B-2 : EC2 Container Service Deep diveの資料です。イベントの様子や他の資料は以下ブログをご覧ください。
http://aws.typepad.com/sajp/2015/12/ivs-cto-night-day-2015-winter-powered-by-aws.html
Build enterprise-grade AI agents with Azure AI Agent ServiceNaoki (Neo) SATO
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Build enterprise-grade AI agents with Azure AI Agent Service (Machine Learning 15minutes! Hybrid #97)
https://satonaoki.wordpress.com/2025/01/25/ml15min_azure_ai_agent_service/
[Machine Learning 15minutes! Broadcast #67] Azure AI - Build 2022 Updates and...Naoki (Neo) SATO
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The document discusses updates to Azure AI and machine learning services from Microsoft. Key updates include new responsible AI tools like a dashboard and scorecard, expanded capabilities for Azure Machine Learning like reusable pipeline components and automated ML for NLP and images, as well as general availability of custom entity recognition, text classification, and document translation. It also previews conversational language understanding and document/conversation summarization.
[Machine Learning 15minutes! #61] Azure OpenAI ServiceNaoki (Neo) SATO
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This video discusses the early history of speech recognition and voice assistants, including IBM's experimental Switchboard system which used cellular networks to allow callers to have spoken conversations with computers over the phone in the 1970s. The Switchboard project helped advance speech recognition and natural language processing but still had significant limitations in understanding full conversations.
[Developers Festa Sapporo 2020] Microsoft/GitHubが提供するDeveloper Cloud (Develop...Naoki (Neo) SATO
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* [Developers Festa Sapporo 2020] Microsoft/GitHubが提供するDeveloper Cloud (Developer Cloud from Microsoft/GitHub)
* https://satonaoki.wordpress.com/2020/12/05/devfesta-microsoft-github/
* https://www.youtube.com/watch?v=sqWnreBtHBg&t=151s
[第2回 Azure Cosmos DB 勉強会] Data modelling and partitioning in Azure Cosmos DB ...Naoki (Neo) SATO
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The document discusses data modeling and partitioning in Azure Cosmos DB. It begins with an overview of Cosmos DB's scalability and flexibility as a non-relational database. It then walks through modeling common entities like customers, products, orders and optimizing the data model and partitioning strategy. The key aspects covered include choosing a partition key, embedding vs referencing data, denormalizing for performance, and using change feeds to keep data synchronized across partitions.
[第45回 Machine Learning 15minutes! Broadcast] Azure AI - Build 2020 UpdatesNaoki (Neo) SATO
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1. Azure AI provides updates on advances in AI capabilities such as object recognition reaching human parity in 2016 and machine translation reaching human parity in 2018.
2. Responsible AI practices at Microsoft include interpretability, fairness, and privacy tools to ensure AI systems are understandable, unbiased, and protect user data.
3. Differential privacy and homomorphic encryption techniques allow training models and performing inferences on encrypted user data to enable private and confidential machine learning.
[db tech showcase Tokyo 2019] Azure Cosmos DB Deep Dive ~ Partitioning, Globa...Naoki (Neo) SATO
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[db tech showcase Tokyo 2019] Azure Cosmos DB Deep Dive ~ Partitioning, Global Distribution and Indexing ~
https://satonaoki.wordpress.com/2019/09/30/dbts2019-azure-cosmos-db-deep-dive/
How to work with technology to survive as an engineer (エンジニアとして生き残るためのテクノロジーと...Naoki (Neo) SATO
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This document provides an overview of Sato Naoki's career and advice for engineers on working with technology. It summarizes Naoki's experience as a software engineer at Oracle and Microsoft, his roles in evangelism and technical writing. It then offers tips for engineers on career design, including keeping skills up to date, learning languages, collaborating in communities, and using public cloud services. The document advocates designing your own career path and paying knowledge forward through sharing.
24. Thumbnail
Service
Thumbnail
ServicePhoto Share
Service
Photo Share
Service
Photo Share
Service
Photo Share
Service
Thumbnail
Service
Photo Share Service
Thumbnail
SharedLib-v7
Photo Share
Service
SharedLib-v1
Photo Share
Service
node.js
Thumbnail
Service
.NET
Photo Share
Service
V1
Thumbnail
Service
V1
Thumbnail
Service
SharedLib-v7
Thumbnail
Service
V2
SharedLib-v1
36. Cloud Services Deployment
Web Application
Web role Web role
Worker Application
Worker role Worker role Worker role Worker role Worker role
Storage queue
Service Bus
Azure cache
Redis
Azure load
balancer
Operational
Insights
Table Storage
Azure SQL
database
Blob Storage
37. Storage queue
Table Storage
Service Bus
Azure SQL
database
Azure cache
Redis
Azure load
balancer
Node
Service
Fabric cluster
Node
Stateless Worker
Service
Node
Node Node
Stateless Web
Service
Stateless Worker
Service
Stateless Worker
Service
Stateless Worker
Service
Stateless Web
Service
Stateless Worker
Service
Operational
Insights
Blob Storage
38. Queues Storage
ステートレス サービス パターン
Front End
(Stateless
Web)
Stateless
Middle-tier
Compute
Cache
Load Balancer