Quite some detail for making AI applications work with the cloud in this new production factory for AI in the Cloud. I like the idea of standardizing such learning projects and installed solutions. I would also like to see this kind of work linked with business process models like BPM.
AI Platform
Create your AI applications once, then run them easily on both GCP and on-premises.
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. ... "
A testimonial they provide:
" ... In retail, it’s important to provide customers with easy access to alternative products or recommended add-ons. We train our own machine learning models with TensorFlow on Google Cloud ML, and we automate the periodic retraining of these models with Kubeflow Pipelines. Together with AI Hub, useful for sharing models between data scientists, we can now iterate faster on our models, and automatically deploy them to staging and production. ... ' Lucas Ngoo, co-founder, CTO, Carousell
See also: https://techcrunch.com/2019/04/10/google-expands-its-ai-services/
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