Optimizing AI Models for Edge Deployment with Nano Banana Training Course
Nano Banana is a lightweight AI framework engineered to accelerate and compress models, ensuring efficient deployment on edge devices and within on-device environments.
This instructor-led live training, available either online or onsite, is designed for professionals with intermediate to advanced expertise. It focuses on optimizing, compressing, and deploying AI models tailored for edge environments using Nano Banana.
Upon completing the program, participants will be equipped to:
- Implement compression and quantization techniques for AI models.
- Enhance inference performance specifically for edge devices.
- Utilize Nano Banana’s toolchain to convert and deploy models.
- Assess the trade-offs between model accuracy, latency, and resource consumption.
Course Format
- Instructor-led technical sessions complemented by guided discussions.
- Hands-on exercises focused on real-world edge-AI scenarios.
- Practical implementation within a pre-configured live environment.
Customization Options
- To receive tailored content or organization-specific adaptations, please contact us to arrange a customized version of this course.
Course Outline
Introduction to Edge AI and Nano Banana
- Essential characteristics of edge-AI workloads
- Overview of Nano Banana’s architecture and capabilities
- Comparative analysis of edge versus cloud deployment strategies
Preparing Models for Edge Deployment
- Model selection and establishing baseline evaluations
- Addressing dependency and compatibility considerations
- Exporting models for subsequent optimization phases
Model Compression Techniques
- Exploring pruning strategies and structural sparsity
- Applying weight sharing and parameter reduction methods
- Assessing the impact of compression on model performance
Quantization for Edge Performance
- Implementing post-training quantization methods
- Managing quantization-aware training workflows
- Utilizing INT8, FP16, and mixed-precision approaches
Acceleration with Nano Banana
- Leveraging Nano Banana accelerators
- Integrating ONNX and hardware backends
- Benchmarking accelerated inference processes
Deployment to Edge Devices
- Integrating models into embedded or mobile applications
- Configuring runtimes and monitoring systems
- Troubleshooting common deployment challenges
Performance Profiling and Trade-off Analysis
- Managing latency, throughput, and thermal constraints
- Balancing accuracy against performance trade-offs
- Applying iterative optimization strategies
Best Practices for Maintaining Edge-AI Systems
- Implementing versioning and continuous updates
- Managing model rollbacks and compatibility
- Addressing security and integrity considerations
Summary and Next Steps
Requirements
- A solid understanding of machine learning workflows
- Experience in developing models using Python
- Familiarity with neural network architectures
Target Audience
- ML engineers
- Data scientists
- MLOps practitioners
Open Training Courses require 5+ participants.
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Testimonials (1)
Flow , vibe and topic on presentation
Lukasz Kowalczyk - Allegro Sp. z o.o.
Course - Google Gemini AI for Data Analysis
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