Data science and machine learning on Cloud AI Platform
Use our suite of tools and services to access a productive data science development environment. AI Platform supports Kubeflow, which lets you build portable ML pipelines that you can run on-premises or on Google Cloud Platform without significant code changes. Access cutting-edge Google AI technology like TensorFlow, TPUs, and TFX tools as you deploy your AI applications to production.
Jump right into your very own Jupyter environment in the cloud by creating a Jupyter notebook on GCP!
Learn how to use Cloud TPUs to speed up your machine learning training and inference.
Ready to run some training and prediction jobs in the cloud? This quickstart guide will show you how to run Keras training jobs with AI Platform.
Ready for a deep dive into deep learning? As you train your neural network to achieve 99% accuracy, you will also discover the tools of the trade that deep learning professionals use to train their models efficiently.
Explore the process of building a complete machine learning pipeline, covering ingest, exploration, training, evaluation, deployment, and prediction. Along the way, explore large data sets using BigQuery and notebooks, process the data in Cloud Dataflow, run distributed training of the model on AI Platform, and deploy the trained model as a microservice.
Learn how to use Kubeflow Pipelines to train a Tensor2Tensor model that predicts issue titles from issue body text. Then deploy and serve your model with TF Serving, and access it from a web app to get predictions.
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