Dive into a comprehensive podcast episode exploring AWS Trainium and Inferentia, powerful AI accelerators designed for enhanced performance and cost savings in machine learning operations. Learn about their seamless integration with popular frameworks like PyTorch, JAX, and Hugging Face, as well as their compatibility with AWS services such as Amazon SageMaker. Gain insights from industry experts Kamran Khan and Matthew McClean as they discuss the benefits of these accelerators, including improved availability, compute elasticity, and energy efficiency. Explore topics ranging from comparisons with GPUs to innovative cost reduction strategies for model deployment and fine-tuning open-source models. Discover how AWS Trainium and Inferentia can elevate your AI projects and transform your approach to MLOps.
AWS Trainium and Inferentia - Enhancing AI Performance and Cost Efficiency
Overview
Syllabus
[] Matt's & Kamran's preferred coffee
[] Takeaways
[] Please like, share, leave a review, and subscribe to our MLOps channels!
[] AWS Trainium and Inferentia rundown
[] Inferentia vs GPUs: Comparison
[] Using Neuron for ML
[] Should Trainium and Inferentia go together?
[] ML Workflow Integration Overview
[] The Ec2 instance
[] Bedrock vs SageMaker
[] Shifting mindset toward open source in enterprise
[] Fine-tuning open-source models, reducing costs significantly
[] Model deployment cost can be reduced innovatively
[] Benefits of using Inferentia and Trainium
[] Wrap up
Taught by
MLOps.community
