Google Cloud Platform

Take your Machine Learning projects to production

AI Platform makes it easy for machine learning developers, data scientists, and data engineers to take their ML projects from ideation to production and deployment, quickly and cost-effectively. From data engineering to “no lock-in” flexibility, AI Platform’s integrated tool chain helps you build and run your own machine learning applications.
AI Platform supports Kubeflow, Google’s open-source platform, which lets you build portable ML pipelines that you can run on-premises or on Google Cloud without significant code changes. And you’ll have access to cutting-edge Google AI technology like TensorFlow, TPUs, and TFX tools as you deploy your AI applications to production.

GCP

Google Cloud Platform Is A Suite Of Public Cloud Computing Services Offered By Google. The Platform Includes A Range Of Hosted Services For Compute, Storage And Application Development That Run On Google Hardware.

Build

Start Building Your Machine Learning Projects Using Al Platform Notebooks. You Can Scale Up Model Training By Using The Cloud ML Engine Training Service In A Serverless Environment Within GCP.

Deploy

Once you have a trained model, Cloud ML Engine offers two types of predictions to apply what the computer learned to new examples. Online Prediction deploys ML models with serverless, fully managed to host that responds in real-time with high availability.

Integrated

Cloud ML Engine Has Deep Integration With Our Managed Notebook Service And Our Data Services For Machine Learning: Cloud Dataflow For Feature Processing, BigQuery For Dashboard Support And Analysis, And Cloud Storage For Data Storage.

HyperTune

Achieve better results faster by automatically tuning deep learning hyperparameters with HyperTune. Data scientists often manage thousands of tuning experiments on the cloud. HyperTune saves many hours of tedious and error-prone work.

Portable Models

Use the open-source TensorFlow SDK or other supported ML frameworks to 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.

“Google Cloud Machine Learning Engine enabled us to improve the accuracy and speed at which we correct visual anomalies in the images captured from our satellites. It solved a problem that has existed for decades. It will allow Airbus Defence and Space to continue to provide unrivaled access to the most comprehensive range of commercial Earth observation data available today”.

- Mathias Ortner

Machine Learning Development: the end-to-end cycle

We empower people to transform complex data, anywhere it resides, into clear and actionable insights

Rather than be an AWS clone, GCP has become a unique services outfit that providing massive-scale services, including artificial intelligence and machine learning. GCP’s advantages today include lower pricing via a sustained-usage discount, a much faster network connecting its data centers, live migration of virtual machines, massive scale, and availability zones, and a variety of redundant backups for always-available storage. What GCP doesn’t offer is the wealth of tools and add-ons that AWS does in its bid to address every use case.

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