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Course Description
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.Training 2 or more people?
Get your team access to the full DataCamp platform, including all the features.- 1
Building Scalable Models with PyTorch Lightning
FreeIn 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.
Introduction to PyTorch Lightning50 xpIntroducing the LightningModule100 xpRunning the Lightning Trainer100 xpDefining models with LightningModule50 xpUsage of the LightningModule50 xpMastering the init method100 xpPerfecting the forward method100 xpImplementing training logic50 xpImplementing the training step100 xpConfiguring the optimizer100 xpTraining and evaluating100 xp - 2
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.
Managing data with LightningDataModule50 xpSplitting data with LightningDataModule100 xpCreating a train DataLoader100 xpIncorporating validation and testing50 xpImplementing the validation step100 xpEvaluate model accuracy using Torchmetrics100 xpEnhancing training with Lightning callbacks50 xpClassifying Lightning callbacks100 xpOptimizing model training with Lightning100 xp - 3
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
Applying dynamic quantization50 xpApply dynamic quantization100 xpComparing quantized model performance100 xpImplementing model pruning techniques50 xpApply pruning to a linear layer100 xpFinalize pruning by removing the mask100 xpExporting models with TorchScript50 xpChoosing the right conversion method50 xpOptimizing models for scalability100 xpRecap: Scalable AI Models with PyTorch Lightning50 xp
Training 2 or more people?
Get your team access to the full DataCamp platform, including all the features.collaborators
prerequisites
Intermediate Deep Learning with PyTorch
Sergiy TkachukDirector of GenAI Productivity at Reckitt
Sergiy is the Director of GenAI Productivity at Reckitt, spearheading AI product initiatives for Global Marketing. He previously led data science teams and co-authored multiple publications as an AI researcher at the Polish Academy of Sciences.
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