Choosing a Cloud Computing Platform for your enterprise

Being an ML Leader

Rahul S


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We cannot do away with clouds. From the perspective of an enterprise, Cloud computing services do not accrue an upfront investment. We get advanced hardware without having to purchase it, and pay per second basis.

They give us access to an on-demand large-scale computing capacity, making it possible to distribute model training across multiple machines. We can also access special hardware configurations, like GPUs, FPGAs, and massively parallel HPC (high performance computing) systems.

Cloud services also provide advanced features for managing datasets and algorithms, training models and deploying them efficiently to production.

(Side note) Infrastructure as a Service (IaaS) is about how vendors deliver cloud-based virtualized resources. The Platform as a Service (PaaS) model adds a layer of managed services to IaaS resources. These PaaS offerings provide the hardware needed for deep learning workloads, as well as software services for managing deep learning pipelines, from data ingestion to production deployment and real-world inference.

To choose which vendor to go for, we have to anticipate our needs with regard to which step in a typical ML workflow matters most to us.