For improved product development, Google Cloud Platform provides safe and cost-effective infrastructure, as well as data and analytics capabilities. In addition, the Google Cloud ML Engine offers training and prediction services that can be used together or separately. Enterprises have used it to tackle challenges as diverse as recognizing clouds in satellite photos, assuring food safety, and replying four times faster to consumer emails. AI Platform Training and AI Platform Prediction are the new names for the training and prediction services in ML Engine.
For more information and a demo, the users can drop a request at the site and get across the demo soon.
Its pricing is not mentioned on the website. You can contact them through email or phone for an understanding of the functionalities of this tool. Its pricing varies based on your need, and you can call them for a quote.
Custom container support - Google can run any other framework on Cloud ML Engine along with native support for popular frameworks like TensorFlow. It simply uploads a Docker container with the training program and Cloud ML Engine that put it to work on Google's infrastructure.
Distributed training - Cloud ML Engine automatically sets up an environment for XGBoost and TensorFlow to run on multiple machines. It gets the speed that is needed by adding multiple GPUs to the training job or splitting it across multiple VMs.
Automatic resource provisioning - Cloud ML Engine is a managed service that automates all resource provisioning and monitoring builds models using managed distributed training infrastructure that supports CPUs, GPUs, and TPUs; and accelerates model development by training across many nodes or running multiple experiments in parallel.
HyperTune - It achieves quick results by automatically tuning deep learning hyperparameters with HyperTune. HyperTune saves many hours of tedious and error-prone work.
Portable models - The open-source TensorFlow SDK or other supported ML frameworks train models locally on sample datasets and use the Google Cloud Platform for training at scale. Models trained using Cloud ML Engine can be downloaded for local execution or mobile integration. It can also import sci-kit-learn, XGBoost, Keras, and TensorFlow models that have been trained anywhere for fully-managed, real-time prediction hosting.