Deep learning requires massive parallel computing capabilities that data centre grade GPUs like Tesla V100 provide. The more data you have, the more computing power it takes to train a model of profound learning.
The truth about creating a model of profound learning is that finding the right set of hyperparameters with only one attempt is difficult. You need to continuously explore.
As a data science professional you know that the creation of learning models will result in underfitting and overfitting. Therefore, you need access to computing power which can help you train models of deep learning fast.
Buying a Deep Learning Machine based on GPU
Buying your own GPU requires upfront costs, and you must stick to the same GPU for a long period of time to get a return on the costs involved. Not to mention that the Tesla V100, the best GPU for deep learning workloads, will cost you over USD 10k.
You need to ensure that the energy and bandwidth requirements are adequate to run your deep learning experiments by getting your GPU server on-prem means.
What if you operate on a wider dataset than you normally do?
You can tap into a GPU cloud services like the GPU Cloud of XcellHost, where you can pay on an hourly and monthly basis as long as you use the GPU services.
In the world of deep learning a lot of developments are happening. The world 's popular GPU manufacturer NVIDIA is increasingly catching up and launching newer and better hardware to meet deep learning computing needs. If you buy your own deep learning machine based on GPU, you can incur 3-year cycles of operating expenses. What if your purchased GPU machine becomes obsolete, or can not perform fast enough?
Rent a deep learning system based on GPU (aka GPU service in the Cloud)
Cloud GPU platforms such as XcellHost's GPU Platform are agile towards the new tech and deep learning space innovations. If NVIDIA launches a GPU better than Tesla V100 tomorrow, GPU service providers will add the innovation to their networks easily, enabling you to tap into the latest technologies without incurring significant upfront costs.
You can run your workloads on multiple GPU instances at much lower cost than acquiring and operating GPU machines on site if you need to cut the time to produce your deep learning models.
When deploying deep learning models in production environments for inferencing, you will need continuous uptime and processing power to adequately serve end-users. On XcellHost, as your situation requires, you can scale up & scale down on demand; this will ensure a seamless experience for your end users.
Summary
If you are performing deep learning experiments with small datasets it would be enough to buy a simple GPU-based computer. You can occasionally tap into a Cloud GPU service if appropriate.
If you're dealing with large data sets and plan to deploy your models in production environments, the best choice for you will be services such as XcellHost's GPU Cloud.
As a side note, XcellHost's GPU Cloud offers GPU instances based on Nvidia T4, Nvidia V100 GPU, Nvidia RTX 8000 and Nvidia DGX A100 cards in the Indian area without compromising on efficiency.