Technical requirementsA quick look at MLExercise – practicing ML code using PythonPreparing the ML dataset by using a table from the BigQuery public datasetTraining the ML model using Random Forest in PythonCreating a batch prediction using the training dataset’s outputThe MLOps landscape in GCPUnderstanding the basic principles of MLOpsIntroducing GCP services related to MLOpsExercise – leveraging pre-built GCP models as a serviceUploading the image to a GCS bucketCreating a detect text function in PythonExercise – using GCP in AutoML to train an ML modelExercise – deploying a dummy workflow with Vertex AI PipelinesCreating a dedicated regional GCS bucketDeveloping the pipeline on PythonMonitoring the pipeline on the Vertex AI Pipelines consoleExercise – deploying a scikit-learn model pipeline with Vertex AICreating the first pipeline, which will result in an ML model file in GCSRunning the first pipeline in Vertex AI PipelinesCreating the second pipeline, which will use the model file from the prediction results as a CSV file in GCSRunning the second pipeline in Vertex AI PipelinesSummary