How to Optimize Models to Send them Right in Production?

If you want to 10x your FPS from an existing model, and learn how to build models that are insanely efficient and ready for production, than read on:

The majority of AI Engineers spend their time working with models that have no chance of ever going to production:
  • They're too slow
  • They're unoptimized for the hardware
  • They don't generalize well

This is why I spent some time working on 2 courses that will help you make models much more efficient and ready for production.

5 Techniques to 10x Your FPS
To be more specific, I found 5 techniques that have the potential to 10x the FPS of your existing model with little work:
  1. Optimizing the Convolutions
  2. Building HydraNets (multi-task learning models)
  3. Pruning your Model
  4. Quantizing your Weights
  5. Implementing Knowledge Distillation

After learning these techniques, clients from my courses had the ability to improve their models by up to 1,268%, and understand how to make their models as optimized as possible.

Here are the two courses:

Course 1 — LEARN HYDRANETS

This first course is about the first two techniques, and it will teach you about Multi-Task Learning and tell you how to build State-Of-The-Art 3D Segmentation Systems With PyTorch.

Course 2 — MASTER NEURAL OPTIMIZATION

The second course will teach the other 3 techniques, and explain you how to build Optimized and Ready-To-Deploy Models with PyTorch

These courses have been built to work together.

This is why I'm also suggesting you to take them in this exact order, one after the other.

And if you're interested in getting both of them at the same time, then I recommend enrolling in the Neural Optimization Pack!