Optimizing AI Models for Edge Deployment with Nano Banana Training Course
Nano Banana is a lightweight AI framework engineered to streamline the acceleration and compression of models, facilitating efficient on-device and edge deployment.
This instructor-led, live training session (available online or onsite) is tailored for intermediate to advanced professionals seeking to optimise, compress, and deploy AI models within edge environments utilising Nano Banana.
Upon completion of this program, participants will be equipped to:
- Implement compression and quantisation techniques for AI models.
- Enhance inference performance specifically for edge devices.
- Transform and deploy models leveraging the Nano Banana toolchain.
- Assess the trade-offs regarding accuracy, latency, and resource consumption.
Course Format
- Instructor-led technical sessions complemented by guided discussions.
- Practical exercises centred on real-world edge AI scenarios.
- Hands-on implementation within a configured live environment.
Course Customisation Options
- For tailored content or organisation-specific adaptations, please contact us to arrange a bespoke version of this course.
Course Outline
Introduction to Edge AI and Nano Banana
- Defining the key characteristics of edge AI workloads
- Exploring Nano Banana architecture and capabilities
- Contrasting edge versus cloud deployment strategies
Preparing Models for Edge Deployment
- Model selection and baseline performance evaluation
- Considering dependencies and compatibility requirements
- Exporting models for subsequent optimisation steps
Model Compression Techniques
- Applying pruning strategies and structural sparsity
- Utilising weight sharing and parameter reduction
- Assessing the impact of compression on performance
Quantisation for Edge Performance
- Implementing post-training quantisation methods
- Integrating quantisation-aware training workflows
- Utilising INT8, FP16, and mixed-precision approaches
Acceleration with Nano Banana
- Leveraging Nano Banana accelerators
- Integrating ONNX and specific hardware backends
- Benchmarking accelerated inference results
Deployment to Edge Devices
- Integrating models into embedded or mobile applications
- Configuring runtime environments and monitoring
- Diagnosing and troubleshooting deployment challenges
Performance Profiling and Trade-off Analysis
- Managing latency, throughput, and thermal constraints
- Balancing accuracy against performance trade-offs
- Developing iterative optimisation strategies
Best Practices for Maintaining Edge AI Systems
- Implementing versioning and continuous update protocols
- Managing model rollback and compatibility
- Addressing security and integrity considerations
Summary and Next Steps
Requirements
- A solid grasp of machine learning workflows
- Proficiency in Python-based model development
- Working knowledge of 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
Provisional Upcoming Courses (Require 5+ participants)
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