This certification validates your ability to build production-ready, AI-powered mobile applications using Google's AI stack (Gemini API, ML Kit, Vertex AI). You will demonstrate competency in integrating generative AI, computer vision, natural language processing, and responsible AI practices into React Native apps.
Duration: 12 weeks (8-10 hours per week)
Prerequisites:
- M1: Mobile Foundations (completed)
- M2: Mobile Platforms (completed and required - M3 evolves M2 projects)
To earn the Mobile AI Developer Certification, you must successfully complete all four components below with minimum passing scores.
Requirements:
Reflection Topics Include:
- When to choose on-device ML vs cloud AI
- Ethical implications of AI features you implemented
- How AI improves user experience in your projects
- Challenges faced integrating AI and solutions applied
- Future applications of learned AI techniques
Passing Standard: Complete all lessons with submitted reflections and discussion participation.
Requirements:
Activity Checklist:
| Lesson |
Activity |
AI Feature |
Pass Criteria |
| 1 |
AI Landscape Exploration |
Compare ML Kit vs Gemini |
Working comparison demo |
| 2 |
Gemini Chat Integration |
Chat with conversation history |
Multi-turn conversations work |
| 3 |
Image Understanding |
Multimodal image analysis |
Describes uploaded images |
| 4 |
Effective Prompting |
Prompt engineering patterns |
Shows improved AI responses |
| 7 |
Object Detection |
ML Kit image labeling |
Labels 5+ objects with confidence |
| 8 |
Document Scanner |
OCR text extraction |
Extracts text from photos |
| 9 |
Face Analysis |
Face detection with landmarks |
Detects faces and landmarks |
| 12 |
Live Translator |
Offline translation |
Translates without internet |
| 13 |
Voice Commands |
Speech recognition |
Executes 5+ voice commands |
| 14 |
Voice Output |
Text-to-speech |
Speaks generated text |
| 17 |
Recommendation Engine |
Personalized suggestions |
Shows relevant recommendations |
| 18 |
Cloud ML Models |
Vertex AI integration |
Calls Vertex AI endpoint |
| 19 |
Mood Detection |
Sentiment analysis |
Analyzes text sentiment |
| 20 |
Ethics Audit |
Bias detection in AI |
Documents bias audit results |
| 21 |
Production AI |
Caching and error handling |
Graceful degradation working |
| 22 |
Cost Monitoring |
API usage tracking |
Displays usage/budget stats |
Passing Standard: Each activity must achieve 70% or higher based on functionality, code quality, and AI implementation rubric.
Requirements:
Evolve M2 SpotFinder with Gemini AI
Required AI Features:
Grading Rubric (100 points):
- Gemini integration (30 points): API working, streaming implemented
- Multimodal features (25 points): Image analysis functional
- Voice features (15 points): Speech recognition/synthesis working
- UI/UX (15 points): Loading states, error handling
- Responsible AI (15 points): Transparency, fallbacks, privacy
Passing Score: 70/100
Evolve M2 ARRoom with ML Kit computer vision
Required AI Features:
Grading Rubric (100 points):
- ML Kit integration (30 points): Image labeling, face detection working
- AR + AI combination (25 points): Overlays based on ML Kit results
- Gemini styling suggestions (20 points): Context-aware recommendations
- Voice control (10 points): Speech commands functional
- Responsible AI (15 points): On-device privacy, clear AI disclosures
Passing Score: 70/100
Evolve M2 FitTrack with AI coaching
Required AI Features:
Grading Rubric (100 points):
- AI coaching (30 points): Personalized, context-aware advice
- Sentiment analysis (20 points): Detects mood from journal
- Recommendations (20 points): Relevant workout suggestions
- Voice coaching (15 points): TTS workout guidance
- Responsible AI (15 points): Health disclaimers, safety checks
Passing Score: 70/100
Full AI-powered social discovery app
Required AI Features:
Grading Rubric (150 points):
- Full AI stack integration (40 points): Gemini + ML Kit + Speech working together
- Production readiness (30 points): Caching, error handling, monitoring
- Responsible AI (25 points): Ethics checklist complete, bias audit done
- User experience (25 points): Seamless AI features, clear disclosures
- Cost optimization (15 points): Budget tracking, efficient API usage
- Documentation (15 points): AI features documented, setup guide
Passing Score: 105/150 (70%)
Special Requirement: 15-minute presentation demonstrating:
- All AI features working
- Responsible AI practices implemented
- Cost/usage monitoring dashboard
- Bias audit results
- Future improvements planned
Requirements:
Topics Covered:
- Gemini API fundamentals and multimodal AI
- ML Kit computer vision (labeling, OCR, face detection)
- Speech recognition and text-to-speech
- Real-time translation and language AI
- Smart recommendations and personalization
- Vertex AI and cloud ML integration
- Sentiment analysis and NLP
- AI ethics and responsible design
- Production AI patterns and cost optimization
Passing Standard: Minimum 70% average score across all Exit Tickets.
Upon certification, you will have demonstrated mastery in:
- ✅ Gemini API integration (text and multimodal)
- ✅ Prompt engineering for consistent results
- ✅ Streaming responses for better UX
- ✅ Conversation history management
- ✅ System instructions and few-shot learning
- ✅ ML Kit image labeling and object detection
- ✅ Optical character recognition (OCR)
- ✅ Face detection and landmark analysis
- ✅ On-device processing for privacy
- ✅ Speech recognition and voice commands
- ✅ Text-to-speech for voice output
- ✅ Real-time offline translation
- ✅ Sentiment analysis and mood detection
- ✅ Caching strategies for cost reduction
- ✅ Rate limiting and quota management
- ✅ Error handling and graceful degradation
- ✅ Monitoring and analytics
- ✅ Cost optimization techniques
- ✅ Bias detection and fairness testing
- ✅ Transparency and user disclosure
- ✅ Privacy-first design patterns
- ✅ Ethical AI implementation
- ✅ Accessibility considerations
Upon successful completion, you will receive:
- Digital certificate of completion
- Verified credential for LinkedIn/resume
- Recognition as Mobile AI Developer
- Listing in course alumni directory
- 4 production-ready AI-powered mobile apps
- Code repositories demonstrating AI skills
- Case studies for each project
- Video demos of AI features
- Industry-ready AI mobile development skills
- Direct pathway to advanced courses (M5, AI4)
- Competitive advantage in mobile AI job market
- Foundation for ML engineering roles
- Alumni network for AI mobile developers
- Monthly AI office hours with instructors
- Early access to new AI course content
- Invitation to annual AI developer summit
Total Estimated Hours: 96-120 hours over 12 weeks
Weekly Breakdown:
- Lessons: 2-3 hours (video content + reading)
- Activities: 3-4 hours (hands-on coding)
- Projects: 2-3 hours (building + testing)
- Exit Tickets: 1 hour (assessment)
Project Weeks (Weeks 3, 6, 9, 12):
- 8-10 hours per project week
- Includes planning, implementation, testing, documentation
| Week |
Assessment |
Weight |
| 1-2 |
Lessons 1-4 + Activities 1-4 + Exit Tickets |
10% |
| 3 |
Project One: SmartSpot submission |
5% |
| 4-5 |
Lessons 7-9 + Activities 5-7 + Exit Tickets |
10% |
| 6 |
Project 2: SmartRoom submission |
5% |
| 7-8 |
Lessons 12-14 + Activities 8-10 + Exit Tickets |
10% |
| 9 |
Project 3: SmartFit submission |
5% |
| 10-11 |
Lessons 17-22 + Activities 11-16 + Exit Tickets |
15% |
| 12 |
Project 4: SmartShare + Presentation |
10% |
| Ongoing |
Weekly reflections + discussion participation |
30% |
Every project must complete this checklist before submission:
- Gemini API Office Hours: Tuesdays 4-5 PM PT
- ML Kit Troubleshooting: Thursdays 3-4 PM PT
- Ethics Review Sessions: Fridays 2-3 PM PT
- Course discussion forum (Slack/Discord)
- Weekly AI implementation showcases
- Peer code review sessions
- Alumni mentorship program
- Email support: support@telebort.com
- Response time: 24-48 hours
- Include error logs, code snippets, screenshots
- Submit through course portal
This certification prepares you for roles including:
- Build AI-powered mobile applications
- Integrate ML models into production apps
- Optimize AI performance and costs
- Avg Salary: $95,000 - $140,000/year
- Design and deploy mobile ML systems
- Train and fine-tune models for devices
- Build AI infrastructure
- Avg Salary: $110,000 - $160,000/year
- Define AI product features
- Implement responsible AI practices
- Balance UX with AI capabilities
- Avg Salary: $100,000 - $150,000/year
- Build end-to-end AI applications
- Integrate cloud and edge AI
- Manage AI product lifecycle
- Avg Salary: $105,000 - $155,000/year
- M5: Mobile Apps That Predict (predictive ML, custom models)
- AI4: Deep Learning & Neural Networks (advanced ML theory)
- Cloud Architecture for AI (scalable AI infrastructure)
- Computer Vision Specialist (image/video AI)
- NLP Engineer (conversational AI, chatbots)
- AI Ethics & Governance (responsible AI leadership)
- Google Cloud Professional ML Engineer
- TensorFlow Developer Certificate
- AWS Certified Machine Learning - Specialty
Certificates include:
- Unique verification code
- QR code linking to public portfolio
- Cryptographic signature
- Skills breakdown and project showcase
Employers can verify: https://verify.telebort.com/[certificate-id]
Q: What if I don't have M2 projects completed?
A: M2 completion is required. You can complete M2 first, or request templates of M2 projects to evolve (slower learning path).
Q: Are API costs covered?
A: All services used have generous free tiers sufficient for the course. Estimated cost: $0-5/month if you exceed free limits.
Q: Can I use OpenAI instead of Gemini?
A: The course teaches Google's AI stack specifically, but concepts transfer. Using alternative APIs acceptable with instructor approval.
Q: How long is the certificate valid?
A: Lifetime validity. Skills remain relevant, but AI technology evolves-consider refresher courses annually.
Q: Can I complete faster than 12 weeks?
A: Yes, self-paced. Minimum 6 weeks (intensive), maximum 24 weeks (extended).
Ready to become a certified Mobile AI Developer? Start your journey today!