Vertex AI Custom Training Guide for Beginners

Vertex AI custom training is useful when you need more control than AutoML or BigQuery ML provides. It lets teams run their own training code in managed infrastructure while still using cloud tracking, artifacts, and deployment workflows.

This guide explains when custom training fits and what beginners should understand before using it.

Quick Answer

Use Vertex AI custom training when you need custom model code, custom dependencies, control over the training environment, distributed training, GPUs, hyperparameter tuning, or a workflow that must match existing ML code.

Use AutoML first when your problem fits a supported AutoML use case and you want a fast baseline.

Key Takeaways

• Custom training gives more flexibility than AutoML.
• Training code should be separate from training data.
• Large datasets should be streamed or loaded incrementally.
• Dependencies can be handled through requirements files, setup files, or containers.
• Model artifacts should be exported after training.
• Local runs can help debug before submitting cloud jobs.

AutoML vs Custom Training

When Custom Training Fits

Use custom training when:

• your use case does not fit AutoML,
• your model needs mixed inputs,
• you need TensorFlow, PyTorch, scikit-learn, or another framework,
• you already have training code,
• you need custom preprocessing,
• you need distributed training,
• you need GPUs,
• you need more control over dependencies,
• you want to tune hyperparameters.

Training Code Structure

A clean training project usually includes:

• training entry point,
• model code,
• data loading code,
• preprocessing logic,
• configuration,
• dependency file,
• output path for artifacts,
• optional evaluation step.

Keep training data outside the code repository. Store code in source control and data in approved data storage.

Pre-built Containers vs Custom Containers

Pre-built containers are easier when your framework and dependencies are already supported. They reduce setup work.

Custom containers are useful when you need:

• a framework version not available in a pre-built image,
• system packages,
• special Python dependencies,
• non-Python training code,
• custom runtime behavior.

Handling Large Datasets

Do not assume the full dataset can fit into memory.

For large datasets:

• stream records,
• read data in batches,
• use framework data pipelines,
• use efficient formats where possible,
• avoid copying huge data into the container image.

Training data should be read from storage, not bundled with the training code.

Exporting Model Artifacts

After training, export the trained model artifacts to durable storage. This is important because downstream steps may need to:

• register the model,
• deploy the model,
• compare model versions,
• rerun evaluation,
• audit how the model was produced.

Practical Beginner Workflow

1. Train locally with a small sample.
2. Confirm the code can load data and save artifacts.
3. Define dependencies.
4. Choose pre-built or custom container.
5. Submit a Vertex AI training job.
6. Store metrics and artifacts.
7. Register or evaluate the model.
8. Decide whether it should be deployed.

Common Mistakes

• mixing training data with source code
• not exporting model artifacts
• using custom containers before they are needed
• not testing locally first
• failing to record parameters and metrics
• ignoring memory limits for large data
• deploying a trained model before validation

Bottom Line

Vertex AI custom training is for teams that need flexibility and production discipline. Start with a simple local run, keep data and code separate, define dependencies clearly, export artifacts, and treat the training job as part of a repeatable ML workflow.