Intelligent Apps with Azure Container Apps (ACA)

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Intelligent Apps with Azure Container Apps (ACA)

加入 Microsoft Reactor 並與開發人員即時互動

準備好開始使用 AI 和最新技術嗎？ Microsoft Reactor 提供活動、訓練和社群資源，協助開發人員、企業家和初創公司建置 AI 技術等等。加入我們！

格式:
線上直播

主題: 程式設計、語言與架構

語言: 英文

此系列中的活動:
2

Build #IntelligentApps today! Kickstart your learning journey to build AI driven cloud-native apps with cloud-scale data and GenAI. Combine the power of AI, cloud-scale data, and cloud-native app development to create highly differentiated digital experience using Azure Container Apps. Learn more at https://aka.ms/intelligent-apps.

Join us and explore the cutting-edge world of intelligent cloud-native microservices using serverless containers on Azure Container Apps. In this talk, we’ll cover some of the latest features in Azure Container Apps and how you can leverage them to architect your scalable, resilient, intelligent applications.

演講者

Simon Jakesch
- Principal Product Manager, DevDiv Texas
Microsoft

此系列中過去的活動

時區 - 國際標準時間

4月

星期四

2024

Build intelligent apps with Azure Container Apps and Qdrant

3:30 下午 - 3:45 下午 (UTC)

Learn how you can use ACA to build a music recommendation service. During this demo you will see how ACA can serve as a platform for AI engineers to process data, and hand off to application engineers to utilize and develop applications around. Learn how to leverage ACA’s Qdrant vector database add-on to ingest and create embeddings for a library of songs. This serves as a backend for a series of polyglot microservices written in Python and Java to develop a recommendation service for music. Explore how ACA’s code to cloud pipeline allows container novices to deploy applications without the use of dockerfiles. If you want to learn more about vector databases, embeddings or how to build microservice applications leveraging AI, then this is for you.

格式:
線上直播

主題: 程式設計、語言與架構

語言: 英文

隨選檢視

4月

星期四

2024

Build a multi-LLM chat application with Azure Container Apps

3:30 下午 - 3:45 下午 (UTC)

In this demo, explore how to leverage GPU workload profiles in ACA to run your own model backend, and easily switch, compare, and speed up your inference times. You will also explore how to leverage LlamaIndex (https://github.com/run-llama/llama_index) to ingest data on-demand, and host models using Ollama (https://github.com/ollama/ollama). Then finally, decompose the application as a set of microservices written in Python, deployed on ACA.