Firebase + AI
Add AI superpowers to your Firebase apps. Learn to integrate Vertex AI and Gemini, use ML Kit for on-device intelligence, build AI-powered Cloud Functions, and implement vector search in Firestore - all within the Firebase ecosystem.
What You'll Learn
By the end of this course, you'll integrate AI capabilities into Firebase apps using Google's full AI stack.
Vertex AI + Gemini
Use the Firebase Vertex AI extension to call Gemini models directly from your client apps with built-in auth.
ML Kit
Run on-device AI with ML Kit for text recognition, face detection, image labeling, and custom TFLite models.
Cloud Functions
Build serverless AI backends with Cloud Functions that call Vertex AI, process data, and trigger on Firestore events.
Vector Search
Implement semantic search in Firestore using vector embeddings and the built-in nearest-neighbor query support.
Course Lessons
Follow the lessons in order or jump to any topic you need.
1. Introduction
Why Firebase for AI? Explore the AI capabilities in the Firebase ecosystem and how they connect to Google Cloud.
2. Setup
Set up a Firebase project with AI extensions. Enable Vertex AI APIs, configure billing, and install the SDK.
3. Vertex AI Extension
Use the Firebase Vertex AI SDK to call Gemini models, handle multimodal inputs, and manage conversations.
4. ML Kit
Integrate ML Kit for on-device AI: text recognition, barcode scanning, face detection, and custom model deployment.
5. Cloud Functions
Build AI-powered Cloud Functions with Vertex AI, Firestore triggers, batch processing, and vector embedding generation.
6. Best Practices
Production patterns for cost management, security rules, model selection, caching, monitoring, and scaling AI workloads.
Prerequisites
What you need before starting this course.
- Basic knowledge of Firebase (Firestore, Authentication, Cloud Functions)
- A Google Cloud account with billing enabled
- Node.js 18 or later installed
- Firebase CLI installed globally
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