11 Building a machine learning pipeline
- 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