This tutorial guides you through deploying a containerized agent built with the Google Agent Development Kit (ADK) to Google Kubernetes Engine (GKE). The agent uses VertexAI to access LLMs. GKE provides a managed environment for deploying, managing, and scaling your containerized applications using Google infrastructure.
This tutorial demonstrates how to deploy the Llama-3.1-8B-Instruct model on Google Kubernetes Engine (GKE) and vLLM for efficient inference. Additionally, it shows how to integrate an ADK agent to interact with the model, supporting both basic chat completions and tool usage. The setup leverages a GKE Autopilot cluster to handle the computational requirements.
This tutorial demonstrates how to deploy the Llama-3.1-8B-Instruct model on Google Kubernetes Engine (GKE) using Ray Serve and vLLM for efficient inference. Additionally, it shows how to integrate an ADK agent to interact with the model, supporting both basic chat completions and tool usage. The setup leverages a GKE Standard cluster with GPU-enabled nodes to handle the computational requirements.
This tutorial will provide instructions on how to deploy and use the Metaflow framework on GKE (Google Kubernetes Engine) and operate AI/ML workloads using Argo-Workflows.
In this tutorial we will fine-tune gemma-2-9b using LoRA as an experiment in MLFlow. We will deploy MLFlow on a GKE cluster and set up MLFlow to store artifacts inside a GCS bucket. In the end, we will deploy a fine-tuned model using KServe.
This tutorial will provide instructions on how to deploy and use FlowiseAI on GKE (Google Kubernetes Engine) to build and operate AI applications using a low-code/no-code approach.