Intelligent Apps with Azure Container Apps (ACA)

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Intelligent Apps with Azure Container Apps (ACA)

Microsoft Reactor に参加し、開発者とライブで交流する

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Intelligent Apps with Azure Container Apps (ACA)

形式:
ライブストリーム

トピック: コーディング、言語、およびフレームワーク

言語: 英語

このシリーズのイベント:
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.

講演者

Simon Jakesch
- Principal Product Manager, DevDiv Texas
Microsoft

このシリーズの過去のイベント

常時 - 協定世界時

4月

木曜日

2024

Build intelligent apps with Azure Container Apps and Qdrant

3:30 午後 - 3:45 午後 (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.

形式:
ライブストリーム

トピック: コーディング、言語、およびフレームワーク

言語: 英語

オンデマンドで見る

4月

木曜日

2024

Build a multi-LLM chat application with Azure Container Apps

3:30 午後 - 3:45 午後 (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.