Optimizing AI Models for Edge Deployment with Nano Banana Training Course
Nano Banana is a streamlined AI framework engineered to streamline and compress models, ensuring high efficiency in on-device and edge settings.
Designed for intermediate to advanced professionals, this live, instructor-led program—available both online and onsite—focuses on optimizing, compressing, and deploying AI models for edge environments via Nano Banana.
Upon completing this program, participants will be equipped to:
- Implement compression and quantization strategies for AI models.
- Enhance inference speed on edge hardware.
- Transform and distribute models leveraging the Nano Banana toolkit.
- Assess the balance between accuracy, response time, and resource consumption.
Course Structure
- Facilitated technical workshops and guided discussions.
- Practical exercises grounded in real-world edge AI applications.
- Live environment-based implementation tasks.
Customization Opportunities
- Contact us to discuss tailored content or organizational adaptations for a customized course version.
Course Outline
Overview of Edge AI and Nano Banana
- Distinct features of edge AI workloads
- Nano Banana framework architecture and core capabilities
- Contrasting edge versus cloud deployment models
Readying Models for Edge Integration
- Selecting appropriate models and establishing performance baselines
- Addressing dependencies and compatibility requirements
- Exporting models to facilitate further optimization
Techniques for Model Compression
- Pruning methodologies and structural sparsity
- Weight sharing techniques and parameter minimization
- Assessing the effects of compression on model quality
Quantization Strategies for Edge Efficiency
- Post-training quantization procedures
- Workflows for quantization-aware training
- Utilizing INT8, FP16, and mixed-precision methods
Performance Acceleration via Nano Banana
- Leveraging Nano Banana acceleration features
- Integration with ONNX and hardware-specific backends
- Conducting benchmarks for accelerated inference
Deploying to Edge Hardware
- Incorporating models into embedded or mobile applications
- Configuring runtimes and setting up monitoring
- Resolving common deployment challenges
Performance Profiling and Balancing Trade-offs
- Managing latency, throughput, and thermal limitations
- Navigating the balance between accuracy and performance
- Applying iterative optimization techniques
Best Practices for Sustaining Edge AI Systems
- Managing version control and continuous updates
- Handling model rollbacks and compatibility issues
- Addressing security and data integrity concerns
Wrap-up and Future Directions
Requirements
- Foundational knowledge of machine learning processes
- Proficiency in Python-based model development
- Working understanding of neural network designs
Target Audience
- Machine Learning Engineers
- Data Scientists
- MLOps Specialists
Open Training Courses require 5+ participants.
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Testimonials (1)
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Lukasz Kowalczyk - Allegro Sp. z o.o.
Course - Google Gemini AI for Data Analysis
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