After doing these courses, I feel confident creating professional visualizations and dashboards
Shoebur RahmanProcurement Specialist, Giz Bangladesh
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.Feels like what you want to learn?
Start Course for FreeWhat you'll learn
- Build, train, and evaluate neural networks using PyTorch Lightning.
- Analyze the key components and benefits of using PyTorch Lightning for AI development.
- Implement advanced techniques in model management and optimization, including pruning, quantization, and logging.
- Implement best practices for data handling and preparation in large-scale AI projects.
Prerequisites
Curriculum
Course outline
1
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.
- Introduction to PyTorch Lightning50 XP
- Introducing the LightningModule100 XP
- Running the Lightning Trainer100 XP
- Defining models with LightningModule50 XP
- Usage of the LightningModule50 XP
- Mastering the init method100 XP
- Perfecting the forward method100 XP
- Implementing training logic50 XP
- Implementing the training step100 XP
- Configuring the optimizer100 XP
- Training 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.
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
Scalable AI Models with PyTorch Lightning
Course
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