Build AI-Powered Apps on Azure Container Apps

6PM - 7PM: Social Hour
7PM - 8PM: Presentation
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Azure Container Apps (ACA) empowers apps to deploy and scale using a managed, serverless model. With its ability to simplify container orchestration, ACA is particularly well-suited web-based and service-oriented apps, but did you know it can also work for AI-powered applications? ACA provides the tools needed to build powerful, cost-efficient AI solutions.

Topics Covered:

What is ACA? Azure Container Apps (ACA) is a serverless container platform that simplifies application deployment and management by abstracting infrastructure concerns. It is designed for microservices, event-driven architectures, and scalable workloads, enabling developers to focus on their applications without worrying about the underlying orchestration.

How does ACA work with AI? ACA integrates seamlessly with Azure AI services, such as Cognitive Services and OpenAI, to power AI applications. It provides a flexible environment to deploy pre-trained models, build inference pipelines, and process data using containerized AI workloads.

ACA Serverless GPU: ACA supports serverless GPUs, which provide high-performance compute for AI tasks like deep learning inference, image processing, and NLP. With pay-as-you-go pricing and autoscaling, developers can efficiently handle computationally intensive workloads.

ACA Add-ons for Vector Database ACA integrates with vector databases like Milvus and QDrant that enable AI applications to perform tasks such as semantic search, recommendation systems, and Retrieval-Augmented Generation (RAG). These databases can be deployed alongside AI models in ACA for low-latency and context-aware solutions.

ACA Scaling for AI-powered Apps: ACA offers robust scaling capabilities, including horizontal scaling to handle traffic spikes and vertical scaling to adjust resource limits like CPU, GPU, and memory. Custom scaling rules optimize performance for AI-specific tasks that ensure resource efficiency and application responsiveness.