Course Overview
This course covers the process of choosing the best public instance for AI workloads on the cloud. It explores the different types of services available for public cloud instances, including Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). The course delves into the nuances of each service, including the differences between instances with and without VNNI, and how hardware considerations, data considerations, model development stages, and optimized software can affect instance selection. Students will learn how to identify AI workload requirements, including hardware, data, and software considerations, and how to choose the right instance size and type for their specific needs. The course also covers methods for deploying AI workloads, including the use of pre-optimized containers and the importance of selecting the right image for Intel processors.
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Chapter Overview
This course covers the process of choosing the best public instance for AI workloads on the cloud. It explores the different types of services available for public cloud instances, including Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). The course delves into the nuances of each service, including the differences between instances with and without VNNI, and how hardware considerations, data considerations, model development stages, and optimized software can affect instance selection. Students will learn how to identify AI workload requirements, including hardware, data, and software considerations, and how to choose the right instance size and type for their specific needs. The course also covers methods for deploying AI workloads, including the use of pre-optimized containers and the importance of selecting the right image for Intel processors.