Access larger dataset faster and easier to accelerate your ML models training in Vertex AI

Vertex AI Training delivers a serverless approach to simplify the ML model training experience for customers. As such, training data does not persist on the compute clusters by design. In the past, customers had only Cloud Storage (GCS) or BigQuery (BQ) as storage options. Now, you can also use NFS shares, such as Filestore, for training jobs and access data in the NFS share as you would files in a local file system. Built-in NFS support for custom training jobs provides the following benefits: Delivers an easy way to store and access large datasets for Vertex AI Training with less of the cumbersome work involving moving training data around. Training jobs execute faster by eliminating the data download steps. Data streams over the network with higher throughput compared to using alternative storage solutions. This article demonstrates how to create a Filestore instance and how to use the data that’s stored in the instance to train a model with your custom training code.  Create a File