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Download Gartner Research – Use This Decision Framework to Determine if Machine Learning Should Run in the Cloud
Gartner analysts Chirag Dekate and Arun Chandrasekaran provide essential guidance to organizations that are assessing the trade-offs involved before deciding whether to run AI workloads on-premises or in the cloud. Infrastructure and Operations Leaders should use this decision framework to determine the right deployment model for machine learning and deep learning use cases.
Here are some Key Points we’d like to point out:
- Minimize training and operationalizing times with the latest accelerator architectures and high-performance compute and storage resources.
- Comprehensively characterize total cost of ownership (TCO), taking into account specialized compute, data storage and data movement requirements.
- Increase efficiency of ML/DL workloads by minimizing data movement and executing ML/DL workloads closer to where the data is generated and stored
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Gartner [Use this Decision Framework to Determine if Machine Learning Should Run in the Cloud], Chirag Dekate, Arun Chandrasekaran October 2017