Navigate to the Vertex AI section of your Cloud Console and click Enable Vertex AI API. Navigate to Compute Engine and select Enable if it isn't already enabled. To create a project, follow the instructions here. You'll need a Google Cloud Platform project with billing enabled to run this codelab. If you are using an older version of TensorFlow, you'll need to import the experimental symbol. Note that as of TensorFlow 2.6, this API is stable. This API allows you to build preprocessing directly into your TensorFlow model graph, reducing the risk of training/serving skew by ensuring that training data and serving data undergo identical transformations. This lab makes use of Keras preprocessing layers to transform and prepare the input data for model training. You'll then load the data into a Jupyter Notebook using pandas and train a TensorFlow model to predict the duration of a cycle trip based on when the trip occurred and how far the person cycled. You'll start by exploring this dataset in BigQuery through the Vertex AI Workbench BigQuery connector. This data contains information about bicycle trips from London's public bikesharing program since 2011. In this lab, you'll explore the London Bicycles Hire dataset. It enables data scientists to connect to GCP data services, analyze datasets, experiment with different modeling techniques, deploy trained models into production, and manage MLOps through the model lifecycle. Vertex AI Workbench helps users quickly build end-to-end notebook-based workflows through deep integration with data services (like Dataproc, Dataflow, BigQuery, and Dataplex) and Vertex AI. This lab will focus on Vertex AI Workbench. Vertex AI includes many different products to support end-to-end ML workflows. You can also migrate existing projects to Vertex AI. The new offering combines both into a single API, along with other new products. Previously, models trained with AutoML and custom models were accessible via separate services. Vertex AI integrates the ML offerings across Google Cloud into a seamless development experience. This lab uses the newest AI product offering available on Google Cloud. The total cost to run this lab on Google Cloud is about $1. Train a model on a Vertex AI Workbench kernel.Use the Vertex AI Workbench BigQuery connector.Create and configure a Vertex AI Workbench instance.In this lab, you'll learn how to use Vertex AI Workbench for data exploration and ML model training.
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