11 Building a machine learning pipeline

This chapter covers

  • An overview of machine learning pipelines
  • Prerequisites for running a machine learning pipeline in Vertex AI
  • Model training and deployment: local implementation vs. machine learning pipeline implementation
  • Defining a machine learning pipeline to train and deploy a model
  • Updating the model training code to work with a machine learning pipeline
  • Using generative AI to help create the machine learning pipeline

11.1 Introduction to ML pipelines

11.1.1 Three kinds of pipelines

11.1.2 Overview of Vertex AI ML pipelines

11.2 ML pipeline preparation steps

11.2.1 Creating a service account for the ML pipeline

11.2.2 Creating a service account key

11.2.3 Granting the service account access to the Compute Engine default service account

11.2.4 Introduction to Cloud Shell

11.2.5 Uploading the service account key

11.2.6 Uploading the cleaned-up dataset to a Google Cloud Storage bucket

11.2.7 Creating a Vertex AI managed dataset

11.3 Defining the ML pipeline

11.3.1 Local implementation vs. ML pipeline

11.3.2 Introduction to containers

11.3.3 Benefits of using containers in an ML pipeline

11.3.4 Introduction to adapting code to run in a container

11.3.5 Updating the training code to work in a container

11.3.6 The pipeline script

11.3.7 Testing the model trained in the pipeline

11.4 Using generative AI to help create the ML pipeline

11.4.1 Using Gemini for Google Cloud to answer questions about the ML pipeline