Get in Touch

Course Outline

Overview of Edge AI and Nano Banana

  • Defining the core attributes of edge-AI workloads
  • Exploring Nano Banana's architecture and capabilities
  • Contrasting edge-based versus cloud-based deployment strategies

Preparation for Edge Deployment

  • Selecting appropriate models and establishing baselines
  • Assessing dependencies and compatibility requirements
  • Exporting models ready for further optimization

Advanced Model Compression Techniques

  • Applying pruning strategies and structural sparsity
  • Utilizing weight sharing and parameter reduction
  • Assessing the impact of compression on model performance

Quantization Strategies for Edge Performance

  • Employing post-training quantization methods
  • Implementing quantization-aware training workflows
  • Applying INT8, FP16, and mixed-precision techniques

Leveraging Nano Banana for Acceleration

  • Operating Nano Banana accelerators effectively
  • Integrating ONNX formats with hardware backends
  • Benchmarking the efficiency of accelerated inference

Deployment on Edge Devices

  • Embedding models into mobile or embedded applications
  • Configuring and monitoring runtime environments
  • Resolving common deployment challenges

Performance Analysis and Trade-off Management

  • Addressing latency, throughput, and thermal limitations
  • Balancing accuracy against performance metrics
  • Adopting iterative optimization approaches

Maintenance Best Practices for Edge-AI Systems

  • Managing versioning and continuous updates
  • Handling model rollbacks and compatibility
  • Ensuring security and data integrity

Conclusion and Future Directions

Requirements

  • A solid grasp of machine learning workflows
  • Proficiency in Python-based model development
  • Knowledge of neural network architectures

Target Audience

  • ML engineers
  • Data scientists
  • MLOps specialists
 14 Hours

Number of participants


Price per participant

Testimonials (1)

Upcoming Courses

Related Categories