Optimizing AI Models for Edge Deployment with Nano Banana Training Course
Nano Banana serves as a lightweight AI framework engineered to streamline model compression and accelerate performance, enabling efficient deployment on both edge devices and directly on hardware.
This live, instructor-led training—available either online or on-site—is tailored for intermediate to advanced professionals seeking to master the optimization, compression, and deployment of AI models in edge environments using Nano Banana.
Upon completing the program, participants will be equipped to:
- Implement compression and quantization strategies for AI models.
- Enhance inference speed specifically for edge-based hardware.
- Utilize Nano Banana’s toolchain to convert and deploy models.
- Assess and balance the trade-offs among model accuracy, response latency, and resource consumption.
Course Delivery Format
- Interactive technical sessions led by an instructor, incorporating guided discussions.
- Practical exercises focused on real-world edge AI scenarios.
- Direct implementation within a fully configured live environment.
Customization Options
- For content tailored to specific organizational needs, please contact us to arrange a customized version of this course.
Course Outline
Foundations of Edge AI and Nano Banana
- Defining the core attributes of edge AI workloads
- Understanding the architecture and core capabilities of Nano Banana
- Analyzing the differences between edge and cloud deployment strategies
Readying Models for Edge Environments
- Selecting appropriate models and establishing baseline evaluations
- Addressing dependencies and compatibility requirements
- Exporting models to prepare for advanced optimization
Advanced Model Compression Methods
- Applying pruning strategies and structural sparsity
- Utilizing weight sharing to reduce parameters
- Measuring the impact of compression on model quality
Quantization for Enhanced Edge Performance
- Employing post-training quantization methods
- Implementing quantization-aware training workflows
- Exploring INT8, FP16, and mixed-precision techniques
Leveraging Nano Banana for Acceleration
- Utilizing Nano Banana’s acceleration features
- Integrating ONNX formats and specific hardware backends
- Conducting benchmarks for accelerated inference
Deploying to Edge Hardware
- Embedding models into mobile or embedded applications
- Configuring runtimes and establishing monitoring systems
- Resolving common deployment challenges
Analyzing Performance and Trade-offs
- Managing latency, throughput, and thermal limits
- Balancing accuracy against performance demands
- Applying iterative optimization strategies
Best Practices for Sustaining Edge AI Systems
- Implementing versioning and continuous update protocols
- Managing model rollbacks and ensuring compatibility
- Addressing security and system integrity concerns
Wrap-Up and Future Directions
Requirements
- A solid grasp of machine learning workflows
- Practical experience in developing models with Python
- Working knowledge of neural network architectures
Target Audience
- ML engineers
- Data scientists
- MLOps practitioners
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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