Optimizing AI Models for Edge Deployment with Nano Banana Training Course
Nano Banana is a lightweight AI framework engineered to compress and accelerate models, ensuring high efficiency in on-device and edge environments.
This live, instructor-led training—available both online and onsite—targets intermediate to advanced professionals aiming to optimize, compress, and deploy AI models in edge settings using Nano Banana.
By the end of this program, participants will be equipped to:
- Implement compression and quantization methods on AI models.
- Enhance inference speed specifically for edge devices.
- Utilize Nano Banana’s toolchain to convert and deploy models.
- Analyze the balance between model accuracy, latency, and resource consumption.
Course Delivery Method
- Interactive instructor-led technical sessions paired with guided discussions.
- Practical exercises based on real-world edge-AI use cases.
- Live implementation within a pre-configured environment.
Customization Opportunities
- For content tailored to specific organizational needs, contact us to arrange a customized version of this course.
Course Outline
Introduction to Edge AI and Nano Banana
- Defining the core characteristics of edge-AI workloads.
- Exploring Nano Banana’s architecture and key capabilities.
- Contrasting edge-based versus cloud-based deployment strategies.
Preparing Models for Edge Deployment
- Selecting appropriate models and conducting baseline evaluations.
- Addressing dependency and compatibility requirements.
- Exporting models to prepare them for further optimization.
Model Compression Techniques
- Applying pruning strategies and structural sparsity.
- Utilizing weight sharing and parameter reduction methods.
- Assessing the impact of compression on model performance.
Quantization for Edge Performance
- Executing post-training quantization methods.
- Implementing quantization-aware training workflows.
- Applying INT8, FP16, and mixed-precision techniques.
Acceleration with Nano Banana
- Leveraging Nano Banana accelerators.
- Integrating ONNX and hardware backends.
- Benchmarking the performance of accelerated inference.
Deployment to Edge Devices
- Embedding models into mobile or embedded applications.
- Configuring and monitoring runtime environments.
- Resolving common deployment challenges.
Performance Profiling and Trade-off Analysis
- Analyzing latency, throughput, and thermal constraints.
- Weighing the trade-offs between accuracy and performance.
- Developing iterative optimization strategies.
Best Practices for Maintaining Edge-AI Systems
- Managing versioning and continuous updates.
- Handling model rollbacks and compatibility management.
- Addressing security and integrity concerns.
Summary and Next Steps
Requirements
- A solid grasp of machine learning workflows.
- Proficiency in Python-based model development.
- Familiarity with various neural network architectures.
Target Audience
- ML Engineers.
- Data Scientists.
- MLOps Specialists.
Need help picking the right course?
uae@nobleprog.com or +971 4871 6715
Optimizing AI Models for Edge Deployment with Nano Banana Training Course - Enquiry
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Course - Google Gemini AI for Data Analysis
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