Posts Tagged ‘AIEvaluation’
[GoogleIO2024] Tune and Deploy Gemini with Vertex AI and Ground with Cloud Databases: Building AI Applications
Vertex AI offers a comprehensive lifecycle for Gemini models, enabling customization and deployment. Ivan Nardini and Bala Narasimhan demonstrated fine-tuning, evaluation, and grounding techniques, using a media company scenario to illustrate practical applications.
Addressing Business Challenges with AI Solutions
Ivan framed the discussion around Symol Media’s issues: rising churn rates, declining engagement, and dropping satisfaction scores. Analysis revealed users spending under a minute on articles, signaling navigation and content quality problems.
The proposed AI-driven revamp personalizes the website, recommending articles based on preferences. This leverages Gemini Pro on Vertex AI, fine-tuned with company data for tailored summaries and suggestions.
Bala explained the architecture, integrating Cloud SQL for PostgreSQL with vector embeddings for semantic search, ensuring relevant content delivery.
Fine-Tuning and Deployment on Vertex AI
Ivan detailed supervised fine-tuning (SFT) on Vertex AI, using datasets of article summaries to adapt Gemini. This process, accessible via console or APIs, involves parameter-efficient tuning for cost-effectiveness.
Deployment creates scalable endpoints, with monitoring ensuring performance. Evaluation compares models using metrics like ROUGE, validating improvements.
These steps, available since 2024, enable production-ready AI with minimal infrastructure management.
Grounding with Cloud Databases for Accuracy
Bala focused on retrieval-augmented generation (RAG) using Cloud SQL’s vector capabilities. Embeddings from articles are stored and queried semantically, grounding responses in factual data to reduce hallucinations.
The jumpstart solution deploys this stack easily, with observability tools monitoring query performance and cache usage.
Launched in 2024, this integration supports production gen AI apps with robust data handling.
Observability and Future Enhancements
The demo showcased insights for query optimization, including execution plans and user metrics. Future plans include expanded vector support across Google Cloud databases.
This holistic approach empowers developers to build trustworthy AI solutions.