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DataCamp

Scalable AI Models with PyTorch Lightning

via DataCamp

Overview

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Streamline your AI projects by building modular models and mastering advanced optimization with PyTorch Lightning!

Foundations of Scalable AI


This course takes you on a journey through the fundamentals of scalable AI. You’ll begin by learning how PyTorch Lightning streamlines the model development lifecycle by reducing boilerplate. Through guided examples, you’ll see how to break complex neural networks into reusable components, allowing you to maintain code quality even as your projects grow in scope.



Advanced Optimization Techniques


You’ll also master optimization techniques, such as adaptive optimizers, model pruning, and quantization. You’ll see firsthand how small changes in training strategy can yield significant gains in speed and accuracy, and you’ll learn how to optimize your training loops to eliminate bottlenecks.



Production-Ready Deployment


By the end of the course, you’ll have gained the skills to take a prototype all the way to production, and you’ll have a portfolio of modular, optimized, and deployable AI solutions ready to tackle real-world challenges.





Syllabus

  • Building Scalable Models with PyTorch Lightning
    • In this chapter, we'll explore how PyTorch Lightning simplifies the development and deployment of scalable AI models. Starting with foundational concepts, we'll go through the core structure of a PyTorch Lightning project, including essential components like the LightningModule and Trainer, to set a strong foundation for more advanced AI solutions.
  • Advanced Techniques in PyTorch Lightning
    • We'll dive deeper into PyTorch Lightning to efficiently manage data and refine model training in this chapter. We'll learn how to create modular and reusable data workflows with LightningDataModule, evaluate your models accurately through validation and testing, and enhance training processes using Lightning Callbacks to automate model improvement and avoid overfitting.
  • Optimizing Models for Scalability
    • Learn to prepare deep learning models for real-world deployment by making them leaner and faster. This chapter introduces techniques such as dynamic quantization, pruning, and TorchScript conversion, helping you reduce model size and latency without sacrificing accuracy

Taught by

Sergiy Tkachuk

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