GOOGLE Cloud Machine Learning Engine in Artificial Intelligence Platform

Google Cloud Platform offers cloud computing services that comprise ML, data analytics, and data storage capabilities. The platform provides features, such as secure and cost-effective infrastructure, along with data and analytics capabilities, for better product development. Moreover, Google offers the open source library, TensorFlow, to carry out numerical calculations with the help of data flow graphs. TensorFlow helps users from various industry backgrounds in using different applications, ranging from language translation to early detection of diseases. Apart from that, Google’s ML Engine enables developers to create ML models on any size of data. Additionally, its Cloud Machine Learning Engine offers managed services that negate the need for infrastructure and provide the basis for model development and prediction. It also adds portability, flexibility, and the ease of use. Additionally, Google’s Cloud Video Intelligence API is designed to concentrate on video analytics for the new audience. Furthermore, the Cloud Video Intelligence API makes the video searching process easier. Request GOOGLE Cloud Machine Learning Engine Pricing to get more information.

Google Cloud ML Engine provide training and prediction services, which can be used together or individually. It has been used by enterprises to solve problems ranging from identifying clouds in satellite images, ensuring food safety, and responding four times faster to customer emails. The training and prediction services within ML Engine are now referred to as AI Platform Training and AI Platform Prediction.

Key Features:

Custom container support

Google can run any other framework on Cloud ML Engine along with native support for popular frameworks like TensorFlow. It simply upload a Docker container with 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 training job or splitting it across multiple VMs.

Automatic resource provisioning

Cloud ML Engine is a managed service which 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 scikit-learn, XGBoost, Keras, and TensorFlow models that have been trained anywhere for fully-managed, real-time prediction hosting.

Server-side pre-processing

It push deployment pre-processing to Google Cloud with scikit-learn pipelines and tf.transform. It can send raw data to models in production and reduce local computation, while also preventing data skew from being introduced through different pre-processing in training and prediction.

Integrated

Cloud ML Engine has deep integration with managed notebook service and data services for machine learning. Cloud Dataflow for feature processing, BigQuery for dashboard support and analysis, and Cloud Storage for data storage.

Multiple Frameworks

Training and Online Prediction support multiple frameworks to train and serve classification, regression, clustering, and dimensionality reduction models.

• scikit-learn for the breadth and simplicity of classical machine learning
• XGBoost for the ease and accuracy of extreme gradient boosting
• Keras for easy, fast deep learning prototyping
• TensorFlow for the cutting-edge power of deep learning