Machine learning Infrastructure as a Service (ML IaaS) is an infrastructure delivery model that provisions virtualized or bare-metal infrastructure resources that are performance optimized for compute-intensive ML and DNN workloads . The ML IaaS market is characterized by core capabilities, including hardware-accelerated, high-performance compute platforms, usually augmented by accelerator technologies like GPU, FPGA or custom processors like Google TPU. Due to the unique nature of hardware-involved DNN frameworks (such as TensorFlow, pyTorch, Caffe and MxNet), they need to be reconfigured and integrated with appropriate libraries to take full advantage of ML IaaS capabilities.
"Best MLIaaS available"
It's an amazing tool, one of the best GPU instances available, and one of the most powerful. Supports pretty much all ML frameworks. Compatible with AWS tools, which is also a huge advantage. Relatively easy to use as everything you need to set up the config is accessible to the browser GUI.
"Google Cloud GPU - cheap and fast solution for ML"
Google Cloud GPU is a great tool to process data that require GPUs for faster processing like images, videos and etc. In our company, we create and improve datasets with ML and with help of Googe Cloud GPU and we choose this one because it's very cost-effective and blazing fast. For example, we use several Nvidia Tesla v100 from Google Cloud GPU for just $X per GPU per hour which is very cheap compared owing one which costs about $Y plus electricity and maintenance costs. Also, Tensorflow and Keras work seamlessly with Google which is GREAT!
"Good company, great team. "
Excellent team, great experience. Very good and stable services.