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Unlock Inference-as-a-Service with Cloud Run and Vertex AI
- Large language models (LLMs) and generative AI have become crucial for applications, often consumed as a service via APIs.
- Inference-as-a-Service eliminates bottlenecks by allowing applications to interface with ML models with low operational overhead.
- Cloud Run, Google Cloud’s serverless container platform, is ideal for driving LLM-powered applications as it leverages container runtimes without infrastructure concerns.
- Using Vertex AI and Cloud Run with GPUs, developers can host open LLMs and access Model Garden offering various ML models.
- By activating the Gemini API in Vertex AI, deploying applications as containers on Cloud Run seamlessly inference with Vertex AI.
- Cloud Run with GPUs offers flexibility, enabling the hosting of LLMs on a serverless architecture for optimized cost and performance.
- Tailoring LLM responses to specific domains can be done using Retrieval-Augmented Generation (RAG), which leverages AlloyDB for contextual customization.
- Inference-as-a-Service manages interactions between Cloud Run, Vertex AI, and AlloyDB, facilitating the RAG data flow in the architecture.
- A chatbot architecture example demonstrates how Cloud Run can host chatbots that inference with LLMs in Vertex AI and store embeddings into AlloyDB.
- Get started with building generative AI Python applications on Cloud Run by following the provided codelab.
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