Outline

[adhiiisetiawan]

Introduction to model optimization for deployment @adhiiisetiawan

• What is model optimization?
• Why is it important for deployment in computer vision?
• Different types of model optimization techniques
• Trade-offs between accuracy, performance, and resource usage

Model compression techniques @mfidabel

• Quantization
• Pruning
• Knowledge distillation
• Low-rank approximation
• Model compression with hardware accelerators

Model deployment considerations @kiluazen

• Different deployment platforms (cloud, edge, mobile)
• Model serialization and packaging
• Model serving and inference
• Best practices for deployment in production

Model optimization tools and frameworks @haizadtarik

• TensorFlow Model Optimization Toolkit (TMO)
• PyTorch Quantization
• ONNX Runtime
• OpenVINO
• TensorRT
• Edge TPU

Model optimization case studies (Hands on, choose one is okay)

• Deploying a real-time object detection model on a mobile device
• Deploying a semantic segmentation model on an edge device
• Deploying a face recognition model for authentication in the cloud
• Case studies of using model optimization techniques for specific deployment challenges

Future trends in model optimization for deployment

• Federated learning
• Continual learning
• Model compression for new hardware architectures

Team Member: @adhiiisetiawan @mfidabel @kiluazen @haizadtarik
cc: @johko @lunarflu @merveenoyan

[lunarflu]

Broadly looks nice a nice outline!

[adhiiisetiawan]

BTW how to add labels "Chapter Content" in this outline?

[lunarflu]

^ I just added it!

[adhiiisetiawan]

Thank youu

[adhiiisetiawan]

Hi guys, we have some questions regarding module 4 "Model optimization tools and frameworks", we planned to create the topics for each tools like this:

• Description of the tools
• Features
• Setup guide
• Example optimization using each tools in simple models (Image recognition) [notebooks]

What do you think? is it okay?

And then, if we already create an example optimization for each tools in module 4, is we still need module 5 "Model optimization case studies"? which is contains specific task like object detection and segmentation?

which is better? notebook at each tools in module 4, or notebook for some tools but in other case (detection and segmentation) in module 5?

We consider about time deadline if we make both on module 4 and 5, maybe you guys have some suggestions or advice. 😅

cc: @johko @lunarflu @merveenoyan

[ATaylorAerospace]

@adhiiisetiawan My feedback is that it might be best to go forward with both Modules and spread the notebooks out ...using module 5 to build upon the foundation that are set with the Example optimization using each tools in simple models in Module 4.

[adhiiisetiawan]

okay, we will make it like that. Thank you @ATaylorAerospace for suggestions