Distributed AI Systems: Scaling Models Across GPUs and Cloud
Level: Advanced · 5 lessons · 96 minutes total · Price: $45.00
Master the advanced techniques and infrastructure required to design, deploy, and manage large-scale AI systems across distributed GPU clusters and cloud environments.
About this course
This advanced course dives deep into the architecture and implementation of Distributed AI Systems, focusing on the practical challenges and solutions for scaling complex machine learning models. Participants will explore state-of-the-art frameworks and strategies for parallelizing training and inference across multiple GPUs and compute nodes, both on-premise and in the cloud. The curriculum covers distributed data parallelism, model parallelism, and pipeline parallelism, along with techniques for efficient data handling, communication, and fault tolerance. You will gain hands-on experience with technologies like PyTorch Distributed, TensorFlow Distributed, Horovod, and cloud-native AI services. The course emphasizes performance optimization, cost-efficiency, and reliability in large-scale AI deployments, preparing you to tackle real-world challenges in MLOps for distributed environments. By the end of this course, you will be equipped to design robust, scalable, and high-performance AI solutions capable of processing massive datasets and handling computationally intensive models.
What you get
- Interactive lessons with quizzes after each module
- AI-generated final exam covering all material
- Personalized PDF certificate upon completion
- Available in 6 languages: English, Arabic, French, Spanish, Russian, Farsi
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