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What you will build

Vertex AI is Google Cloud’s managed platform for developing, training, evaluating, and serving machine-learning models. For students, its value is not simply access to large models: it teaches the complete workflow used in real projects, from data preparation and experiment tracking to deployment, monitoring, and responsible use.

In this tutorial, you will create a small tabular classification project. The same workflow can later support text, image, forecasting, or generative AI applications. If you are still choosing a project, compare this exercise with ideas in our guide to best machine learning projects for computer science students.

Before you start

You need:

• A Google account and a Google Cloud project.
• A billing account attached to the project. Google Cloud credits or student programmes may help, but availability and limits vary.
• Basic Python, pandas, and machine-learning knowledge.
• A small CSV dataset with a clearly defined target column.
• A laptop with a modern browser; most training work runs in Google Cloud.

Treat billing as part of the technical setup. Create a separate student project, set a budget alert, and delete idle endpoints and notebooks after each session. A budget alert is not always a hard spending cap, so check the pricing page and your billing dashboard rather than assuming alerts stop usage.

Create and secure the project

1. Open the Google Cloud Console and create a new project.
2. Select a region close to your users or institution, while checking that the Vertex AI features you need are available there.
3. Link billing and enable the Vertex AI API. You may also need Cloud Storage and, depending on the workflow, Compute Engine APIs.
4. Create a Cloud Storage bucket in the same region. Use a distinctive name and keep its permissions private.
5. Prefer a dedicated user account or service account with only the roles required for the lab. Do not paste service-account keys into notebooks or public repositories.
6. Set a budget alert and record the project ID, region, and bucket URI in your project README.

For group assignments, define who can read data, submit jobs, deploy models, and delete resources. This prevents accidental exposure of personal information and makes the project easier to hand over.

Prepare a small dataset

Start with a dataset that is modest in size and easy to explain. For example, predict whether a student completes a course from attendance, assignment completion, and previous scores. Do not use identifiable student records without formal approval; anonymised or public data is safer.

Your preparation checklist:

• Remove names, phone numbers, email addresses, and unnecessary identifiers.
• Separate features from the target you want to predict.
• Inspect missing values, duplicate rows, and unusual categories.
• Split data into training and test sets before making claims about performance.
• Look for data leakage, such as a feature that would only be known after the outcome.
• Record the dataset source, licence, cleaning steps, and limitations.

Upload the final training file to your private Cloud Storage bucket. For a first project, Vertex AI’s managed tabular workflows can be easier than writing a custom training container, while a custom Python training job gives you more control over preprocessing and model code.

Train a model in Vertex AI

In the Cloud Console, open Vertex AI and create a dataset appropriate to your data type. For a CSV, choose a tabular dataset and import the file from Cloud Storage. Identify the target column, review detected data types, and start training with a small budget and a limited training time.

During configuration, pay attention to:

• Objective: classification and regression require different evaluation metrics.
• Training budget: begin small; more compute does not automatically fix poor data.
• Feature handling: inspect how missing values and categorical columns are treated.
• Data split: use a reproducible split where possible.
• Evaluation metrics: accuracy alone can be misleading for imbalanced classes. Review precision, recall, F1 score, and the confusion matrix.

If you use the Python SDK, keep configuration in a script rather than an untracked notebook. Pin package versions, set a random seed where supported, and save the training configuration with each experiment. These habits make your result reproducible when you submit it for assessment or include it in a portfolio.

Evaluate beyond one score

A model is not ready merely because training finished. Inspect the evaluation results and ask:

• Does performance hold across relevant groups or categories?
• Are false positives or false negatives more harmful for this use case?
• Is the test set representative of the people or conditions where the model will be used?
• Does a simple baseline perform almost as well?
• Can you explain the main features influencing predictions without overstating causation?

For student projects, a short error analysis is often more valuable than another round of tuning. Include five to ten incorrect predictions, explain likely causes, and propose a data or feature change. This demonstrates engineering judgement rather than leaderboard chasing.

Deploy and test an endpoint

To serve online predictions, register the model in Vertex AI Model Registry and deploy it to an endpoint. Select the smallest suitable machine type and avoid leaving the endpoint running when you are not testing. Deployment can cost more than local experimentation because the serving infrastructure may continue running.

Send a prediction request using the exact feature format expected during training. Test valid input, missing values, unexpected categories, and multiple requests. Keep a record of latency, response format, and errors. Never send real sensitive data to a classroom endpoint unless your institution has approved the data handling.

For a low-cost demonstration, you can often evaluate the model in batch rather than maintaining a continuously available endpoint. When the lab is complete, undeploy the model, delete unused endpoints and jobs, and remove temporary storage objects.

Add responsible AI and documentation

A credible Vertex AI project includes a model card or README covering the purpose, dataset, intended users, known limitations, metrics, and safe-use boundaries. If your model makes recommendations about education, finance, health, or employment, state clearly that it should support—not replace—human judgement.

You can extend the project into a portfolio piece by comparing it with building open-source AI projects for students in India, publishing only synthetic or permitted data, and including reproducible setup instructions. Students exploring practical applications can also study building Gen AI consumer apps for students in India, but should apply the same privacy, evaluation, and cost controls.

A useful submission structure

Organise your repository or report as follows:

• README.md: problem, setup, usage, and limitations.
• data/: schema or sample data only; never commit private records.
• notebooks/: exploration and visual analysis.
• src/: preprocessing, training, and prediction code.
• configs/: region, model, and experiment settings.
• reports/: metrics, error analysis, and screenshots.
• requirements.txt or pyproject.toml: pinned dependencies.

A strong final presentation explains why you chose the problem, how Vertex AI reduced infrastructure work, what failed, how much the experiment cost, and what you would improve next.

Common questions

Is Vertex AI suitable for beginners?

Yes, if you begin with a small dataset and understand the difference between training, evaluation, and deployment. The platform manages infrastructure, but it does not replace decisions about data quality or model validity.

Can students use Vertex AI at no cost?

Some Google Cloud products and educational programmes may provide credits or limited usage. Terms change, and many Vertex AI resources are billable. Check current pricing, configure alerts, and shut down resources after every lab.

Should I use AutoML or custom training?

Use managed AutoML-style workflows for a fast, explainable first project. Choose custom training when you need a specific library, architecture, preprocessing pipeline, or reproducibility requirement.

What should I learn next?

Build a second project with a different data type, join a 2026 AI hackathon for Indian engineering students, or explore an AI platform for Indian students planning higher studies abroad to understand how AI products are evaluated beyond a model score.