E2E GPU cloud makes it easy and affordable for deploying machine learning, deep learning workloads. E2E provides an option either to go with GPU.T4 or GPU.V100 card variants. They are mostly used for deep learning, machine learning, and video rendering. Both the processor comes with Tensor cores. GPU Tesla T4 has 2560 CUDA cores, whereas Tesla V100 has 5120 CUDA Cores.
GPU based deployment can help to speed up machine learning workload by order of magnitude, hours, day, and months. Machine learning is easy with open-source frameworks, which makes the workloads more productive and less time taking.
Startups can easily start with already available datasets using machine learning models. Cloud-based GPUs are a great opinion to run machine learning workloads. GPU cards work more efficiently than CPU cores as they can do multiple processing at the same time and run various instructions simultaneously.
Machine learning is the best use case of the cloud server that can handle big data. We have various benefits where machine learning is useful for us. Other use cases for machine learning include Data sciences, automation, medical imaging, weather forecasting, and analytics.
Benefits of using machine learning in the E2E cloud:
1. E2E GPU cloud model is used for AI, machine learning workloads. It makes it easy for experimenting with enterprise machine learning capabilities.
Machine learning divides the learning process into tiny steps that will be very helpful for us.
2. The hourly billing model is also available for training purposes.
3. E2E GPU Cloud is easily scalable as per the utilization.
4. Ready to use images available where the user can directly launch the server from these images.
5. Tensorflow libraries are preinstalled with the GPU servers of E2E.
6. Jupyter Notebook integration is also available making it easier to save the data.
To know more about our GPU servers, check the link here