GCP ML – CloudAge

Google Cloud Platform

Machine Learning is functionality that helps software perform a task without explicit programming or rules.

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.

“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

Data Analysis Image Processing Lead, Airbus Defence & Space.

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.