Building a scalable MLOps system with Vertex AI AutoML and Pipeline

When you build a Machine Learning (ML) product, consider at least two MLOps scenarios. First, the model is replaceable, as breakthrough algorithms are introduced in academia or industry. Second, the model itself has to evolve with the data in the changing world.  We can handle both scenarios with the services provided by Vertex AI . For example: AutoML capability automatically identifies the best model based on your budget, data, and settings. You can easily manage the dataset with Vertex Managed Datasets by creating a new dataset or adding data to an existing dataset.   You can build an ML pipeline to automate a series of steps that start with importing a dataset and end with deploying a model using Vertex Pipelines. This blog post shows you how to build this system. You can find the full notebook for reproduction here . Many folks focus on the ML pipeline when it comes to MLOps, but there are more parts to building MLOps as a “system”. In this post, you will see how Google Cloud Stor