From the course: Scalable Data Storage and Processing for AI Workloads
Join today to access over 24,800 courses taught by industry experts.
Best practices to optimize AI workloads
From the course: Scalable Data Storage and Processing for AI Workloads
Best practices to optimize AI workloads
- [Instructor] Let's now discuss some of the best practices that you can follow to optimize your AI workload. Make sure you utilize advanced computing systems with high performance GPUs, TPUs, or other accelerators to enhance the efficiency and speed of AI training and inference tasks. Leverage parallelization and distributed computing. Distribute AI workloads across multiple nodes using frameworks like TensorFlow Distributed or Horovod to significantly reduce training times and optimize resource usage. Adopt data pre-processing pipelines. Streamline your data pre-processing with tools like Apache Spark, TensorFlow data APIs, or custom ETL pipelines to efficiently clean, normalize, and prepare data. Optimize your networking infrastructure, and we discuss this while discussing storage systems as well. Make sure you have low latency, high bandwidth connectivity for distributed AI workloads by investing in optimized networking solutions such as InfiniBand or high-speed ethernet. Optimize…
Practice while you learn with exercise files
Download the files the instructor uses to teach the course. Follow along and learn by watching, listening and practicing.
Download courses and learn on the go
Watch courses on your mobile device without an internet connection. Download courses using your iOS or Android LinkedIn Learning app.