Google announce end to end enterprise ready AI Platform

At Cloud Next’19, an ongoing event at San Francisco, Google launched AI platform aims to make easy for machine learning developers, data scientists, and data engineers to take their ML projects from idea to production, quickly and cost-effectively.


AI Platform supports Kubeflow, which lets you build portable ML pipelines. That you can run on-premises or on Google Cloud without significant code changes. As you deploy your AI applications to production. you’ll have access to Google AI technology like TensorFlow, TPUs, and TFX tools.

Machine learning development: the end-to-end cycle

end to end machine learning development cycle

Preparing data

Through storing your data in Cloud Storage or BigQuery. you can use the built-in data labeling service to label your training data. and applying classification, object detection, and entity extraction, etc. You can also import the labeled data to AutoML and train a model.

Build and Run

You can build your ML applications on GCP with a managed Jupyter Notebook. that provides configured environments for different ML frameworks.

You can manage your models, experiments, and end-to-end workflows using the AI Platform interface within the GCP console.


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