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.

The practical value of custom training is control. The tradeoff is responsibility: teams must manage code quality, dependencies, data access, artifacts, evaluation, and deployment readiness.

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.
• Training jobs should leave enough evidence to compare, reproduce, and approve the model later.

AutoML vs Custom Training

When To Use AutoML, BigQuery ML, Or Custom Training

Custom training is powerful, but it is not always the first choice.

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.

It is also a good fit when a team already has training code from notebooks, local development, or another ML platform and wants to move that code into a managed cloud training job.

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.

For beginners, the most important habit is to separate concerns:

• code belongs in version control,
• data belongs in approved storage,
• configuration should be explicit,
• model outputs should go to a durable artifact location,
• metrics should be recorded for review.

This makes the training job easier to debug, rerun, and explain later.

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.

Start with a pre-built container if it fits. Move to a custom container when dependency or runtime control becomes a real requirement, not just because it feels more advanced.

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.

For production workflows, also think about:

• whether the training job has permission to read the data,
• whether the data snapshot is traceable,
• whether sensitive data is handled correctly,
• whether feature calculations match prediction-time logic,
• whether failed records are logged clearly enough to debug.

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.

Useful outputs can include:

• trained model files,
• evaluation metrics,
• configuration values,
• training logs,
• preprocessing artifacts,
• feature metadata,
• container image reference,
• model card or release notes.

Artifacts are not just files. They are evidence of what happened during training and why a model version was considered good enough to review or deploy.

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 a pre-built or custom container.
5. Confirm permissions for data, storage, and artifact output.
6. Submit a Vertex AI training job.
7. Store metrics, logs, configuration, and model artifacts.
8. Compare results against the baseline.
9. Evaluate the model before registration or deployment.
10. Decide whether it should be deployed, retrained, or rejected.

Real-World Example

Imagine a team building a document classification model for internal support tickets. AutoML gives the team a quick baseline, but the results are not good enough for the categories that matter most. The team needs custom preprocessing because ticket text includes product names, error codes, account types, and internal shorthand.

Custom training fits because the team wants to control the text cleaning, model architecture, class weights, validation split, and evaluation metrics. The first version runs locally on a small sample. Once the code can load data, train, evaluate, and export artifacts, the team moves it into a Vertex AI custom training job.

The team stores training data in approved storage, keeps code in source control, and writes model outputs to a durable artifact path. After training, it compares the new model against the AutoML baseline. The team does not deploy only because the training job completed. It checks whether the model improves recall for high-priority ticket categories, whether false positives are acceptable, and whether the model can be explained to support managers.

This is the right mindset for custom training. The goal is not only to run code in the cloud. The goal is to create a repeatable training workflow that produces metrics, artifacts, and evidence for a deployment decision.

Training Job Checklist

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
• using a custom container before it is needed
• hardcoding data paths and output locations
• forgetting to compare against AutoML or a simple baseline
• treating a completed training job as a deployment approval

Official Resources

• Vertex AI custom training documentation
• Vertex AI pre-built containers
• Vertex AI custom containers
• Google Cloud MLOps guidance

Related AI Charcha Reading

• Vertex AI Pipelines and ML Artifacts Guide
• Hyperparameter Tuning with Vertex Vizier Guide
• Vertex AI Prediction and Model Monitoring Guide
• Vertex AI Feature Store Guide
• Enterprise Machine Learning Workflow Guide

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.

A good custom training job should not only produce a model. It should also preserve the code, configuration, metrics, logs, and artifacts needed to understand why that model should be trusted.