Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Duration 14 hours
Course Outline
Foundations of Edge AI and Nano Banana
- Defining attributes of edge-AI workloads
- Exploring Nano Banana's architecture and features
- Analyzing edge versus cloud deployment approaches
Readying Models for Edge Integration
- Selecting models and assessing baseline performance
- Addressing dependency and compatibility factors
- Preparing models for export and further optimization
Applying Model Compression Methods
- Exploring pruning techniques and structural sparsity
- Utilizing weight sharing and parameter reduction
- Assessing the impact of compression
Leveraging Quantization for Edge Efficiency
- Post-training quantization strategies
- Workflows for quantization-aware training
- Applying INT8, FP16, and mixed-precision methods
Accelerating Performance with Nano Banana
- Leveraging Nano Banana accelerators
- Connecting with ONNX and hardware backends
- Benchmarking accelerated inference results
Deploying to Edge Hardware
- Embedding models into mobile or embedded applications
- Configuring and monitoring runtime environments
- Resolving common deployment challenges
Analyzing Performance and Trade-offs
- Managing latency, throughput, and thermal limits
- Balancing accuracy against performance metrics
- Employing iterative optimization tactics
Best Practices for Edge-AI System Maintenance
- Managing version control and continuous updates
- Handling model rollbacks and compatibility
- Addressing security and integrity needs
Recap and Future Directions
Requirements
- A solid grasp of machine learning workflows
- Proficiency in Python-based model development
- Knowledge of neural network structures
Target Audience
- ML Engineers
- Data Scientists
- MLOps Specialists
Testimonials (1)
Flow , vibe and topic on presentation
