Cloud services and software-as-a-service help build high-performance digital enterprises. With the increasing adoption of artificial intelligence, organizations need to consider how to deploy machine learning (ML) and deep learning (DL) workloads. With respect to cloud migration, there are two options—virtual machines (VMs) or serverless cloud deployment. Here are a few points to consider for high performance at optimal costs:
- VM-based platforms perform better for static and consistent workloads
- Serverless platforms maintain a steady response time, irrespective of increase in workloads
- VM platforms perform better for long-duration workloads with higher resource utilization
- Serverless platforms are ideal for short-duration workloads with low resource utilization
- ML platforms are easy to deploy but harder to scale as operations grow
- Serverless platforms are more scalable and better for ‘bursty’ or variable workloads
Serverless computing is more cost effective and may be the best way forward to support complex AI applications. Read the white paper to delve into the heart of this.
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