Optimizing AI Models for Edge Deployment with Nano Banana Training Course
Nano Banana is a streamlined AI framework engineered to compress and accelerate models, ensuring optimal performance for on-device and edge deployment scenarios.
This live, instructor-led training—available either online or onsite—is tailored for mid-to-senior-level professionals seeking to refine, compress, and deploy AI models within edge environments leveraging Nano Banana.
Upon completion of the program, participants will be equipped to:
- Implement compression and quantization techniques for AI models.
- Enhance inference speed specifically for edge hardware.
- Utilize the Nano Banana toolchain for model conversion and deployment.
- Analyze the balance between model accuracy, latency, and resource consumption.
Course Structure
- Technical workshops led by instructors, paired with guided discussions.
- Practical exercises focused on real-world edge-AI use cases.
- Implementation tasks performed within a pre-configured live environment.
Customization Availability
- Contact us to discuss bespoke content or organizational adaptations for a tailored course version.
Course Outline
Fundamentals of Edge AI and an Introduction to Nano Banana
- Defining the core traits of edge-AI workloads.
- Exploring Nano Banana’s architecture and functional capabilities.
- Contrasting edge versus cloud deployment strategies.
Readying Models for Edge Integration
- Choosing models and establishing performance baselines.
- Assessing dependencies and hardware compatibility.
- Preparing model exports for further refinement.
Techniques for Model Compression
- Applying pruning methods and achieving structural sparsity.
- Utilizing weight sharing to reduce parameter count.
- Measuring the effect of compression on model quality.
Quantization Strategies for Edge Efficiency
- Applying post-training quantization techniques.
- Designing quantization-aware training pipelines.
- Implementing INT8, FP16, and mixed-precision methods.
Accelerating Performance with Nano Banana
- Leveraging Nano Banana’s acceleration features.
- Integrating with ONNX and specific hardware backends.
- Conducting benchmarks on accelerated inference tasks.
Deploying to Edge Hardware
- Embedding models into mobile or embedded applications.
- Configuring runtime settings and monitoring systems.
- Resolving common deployment challenges.
Analyzing Performance and Trade-offs
- Managing latency, throughput, and thermal limits.
- Balancing accuracy against performance metrics.
- Employing iterative approaches to optimization.
Maintaining Robust Edge-AI Systems
- Managing version control and continuous updates.
- Handling model rollbacks and compatibility issues.
- Addressing security and system integrity requirements.
Wrap-up and Recommended Next Steps
Requirements
- A solid grasp of machine learning workflows.
- Practical experience developing models in 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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