Optimizing AI Models for Edge Deployment with Nano Banana Training Course
Nano Banana serves as a streamlined AI framework engineered to boost speed and reduce model size, facilitating efficient execution on on-device and edge infrastructure.
This live, instructor-facilitated program, available both online and on-site, targets professionals at intermediate to advanced levels who aim to refine, compress, and integrate AI models into edge environments leveraging Nano Banana.
By the end of the training, participants will be equipped to:
- Implement compression and quantization strategies for AI models.
- Enhance inference speeds specifically for edge hardware.
- Leverage Nano Banana’s specialized toolchain for model conversion and deployment.
- Assess the balance between accuracy, response time, and resource consumption.
Course Delivery Method
- Facilitated technical workshops accompanied by guided discussions.
- Practical labs based on real-world edge-AI applications.
- Live environment configuration for hands-on implementation.
Tailoring the Course Content
- Contact us to discuss customized content or specific organizational adaptations for this program.
Course Outline
Overview of Edge AI and the Nano Banana Framework
- Defining the unique demands of edge-AI tasks
- Exploring Nano Banana's design and functional capabilities
- Contrasting edge-based versus cloud-based deployment methods
Readying Models for Edge Integration
- Selecting appropriate models and establishing performance baselines
- Addressing dependencies and system compatibility
- Exporting models to prepare for advanced optimization
Strategies for Model Compression
- Applying pruning tactics and achieving structural sparsity
- Utilizing weight sharing and minimizing parameters
- Assessing the impact of compression techniques
Quantization to Boost Edge Efficiency
- Employing post-training quantization methods
- Managing quantization-aware training processes
- Exploring INT8, FP16, and mixed-precision strategies
Speeding Up Inference with Nano Banana
- Leveraging Nano Banana's acceleration features
- Connecting ONNX formats with specific hardware backends
- Testing the performance of accelerated inference
Integrating Models into Edge Devices
- Embedding models within mobile or embedded software
- Configuring runtime settings and overseeing operations
- Resolving common deployment challenges
Performance Analysis and Balancing Trade-offs
- Managing latency, data throughput, and thermal limits
- Balancing model accuracy against operational speed
- Implementing iterative refinement techniques
Best Practices for Sustaining Edge-AI Systems
- Handling version control and ongoing updates
- Managing rollbacks and ensuring compatibility
- Addressing security and data integrity concerns
Conclusion and Future Directions
Requirements
- Proficiency in machine learning processes
- Practical experience with Python-based model creation
- Knowledge of neural network designs
Target Audience
- ML Engineers
- Data Scientists
- MLOps Specialists
Open Training Courses require 5+ participants.
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Lukasz Kowalczyk - Allegro Sp. z o.o.
Course - Google Gemini AI for Data Analysis
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