Leveraging Vertex AI for end-to-end ML workflows

Using Vertex AI pipelines to automate and orchestrate the ML lifecycle

A Vertex AI pipeline connects the steps of machine learning into a single automated workflow. Each pipeline step is a component with a clear job, and the output of one step becomes the input of the next. For example, data preparation produces a training dataset, training produces a model artifact, evaluation measures the model, and model registration makes the model available for serving. Because the pipeline records the inputs, outputs, and parameters of each run, teams can see exactly how a model was created and can rerun or compare experiments.

Pipelines make the ML lifecycle manageable in production. A pipeline can be started manually, on a schedule, or by an event such as new data arriving. Each run moves through states such as pending, running, and succeeded or failed, and a failed run can be retried from the failing step rather than starting over. For an architect, the main value is using a pipeline as the single path from data to service instead of treating training and deployment as separate manual projects.

Preparing for Vertex AI data integration

Before training, the data must be in a location and format that Vertex AI can read. Common sources are Cloud Storage buckets for files and BigQuery tables for analytics data. The data also needs to be split into training, validation, and test sets so the model can be evaluated fairly on data it has not already seen. During preparation, the data may need to be cleaned, transformed, labeled, or checked for missing values.

Data integration is about making this process ready for repeated use. Permissions define which users and pipeline steps can read or update the data, and versioning helps tie each model run to the exact dataset that was used. A well-designed integration keeps the path from source data to pipeline input stable, so new data can feed retraining without manual copying or formatting.

Using AI Hypercomputer (e.g., using AI Hypercomputer, Cloud Run functions, and Vertex AI for ML/AI workloads; integrating GPUs and TPUs in ML model training and serving; optimizing for different consumption models; and running large-scale AI model training)

AI Hypercomputer is Google Cloud's infrastructure for large-scale AI work. It combines compute, storage, networking, and software so that ML training and serving can run at high performance. Vertex AI acts as the platform layer that submits, monitors, and manages these workloads, while Cloud Run functions can be used as event-driven glue, such as starting a training job or sending a prediction request when data changes.

Accelerators are an important part of this section. GPUs support many kinds of model training and serving and are flexible for different model shapes. TPUs are optimized for very large, highly parallel workloads and can provide speed and efficiency when the model fits the TPU's design. The choice depends on model size, data size, latency requirements, and cost. For large-scale training, the whole system must work together: data must be fed quickly enough, checkpoints must be saved, and the job must scale across many accelerators without leaving expensive resources idle.

Consumption models add a cost and reliability decision. On-demand resources give flexibility for short or unpredictable jobs, committed-use options give lower cost for work that runs continuously, and spot or preemptible resources give the lowest cost for jobs that can be interrupted and restarted. Architects should match the consumption model to how important and how repeatable the workload is, and they should also decide whether prediction traffic needs accelerators for low latency or CPUs for less expensive serving.