Demo: Taking Gemma from prototype to production faster with Vertex AI
summary
A straight runthrough of taking Gemma from Model Garden to a production endpoint using Vertex AI, showing downloading the model (~20 minutes), LoRA fine-tuning on a suggested OpenAssistant/Guanaco dataset, and a tuning job that typically runs 20–60 minutes, then one‑click deployment to a managed endpoint with built‑in monitoring and a cleanup option that deletes resources. Notebook demos use Colab Enterprise, recommend 4‑bit quantization and L4 hardware, and surface the API request format, license details, and model card eval and safety metadata.
AI-written summary · about
chapters (4)
- 0:00Vertex AI and Gemma intro
- 3:56Deploying Gemma demo
- 6:49Fine-tuning Gemma with LoRA
- 9:56Deploying and monitoring model
related talks
44mBuilding amazing Front-End Project with Gemma: A State-of-the-Art Open Model
39mFlutter Project with Gemma: A State-of-the-Art Open Model
40mBuilding amazing Front-End Project with Gemma: A State-of-the-Art Open Model
49mOpen Models: The Secret Weapon for Next-Generation Software
31mFrontier results, on device
43mRunning open large language models in production with Ollama and serverless GPUs
31mBringing AI to Your Data
14mGemma on mobile and web. Best and worst practices