In the world of machine learning, building a high-performing model is just one part of the puzzle. The real challenge often lies in deploying that model into a production-ready environment. Fortunately, Vertex AI, Google Cloud’s machine learning platform, is designed to simplify the entire lifecycle of ML development—from training to deployment.
Whether you’re new to machine learning or an experienced practitioner, VertexAI offers powerful tools that are both flexible and easy to use. In this article, we’ll explore how to train and deploy models using Vertex AI, and why it’s becoming a go-to solution for ML engineers.
What Is Vertex AI?
VertexAI is Google Cloud’s managed machine learning platform that brings together all the services needed for building, training, testing, and deploying ML models at scale. It supports both AutoML (for users without deep ML expertise) and custom model training (for developers using frameworks like TensorFlow or PyTorch).
Key Features of Vertex AI:
- Unified dashboard for data, models, and deployments
- AutoML for fast, code-free model training
- Support for custom containers and Jupyter notebooks
- End-to-end workflow automation
- Built-in tools for monitoring and explainability
Step 1: Organize and Prepare Your Data
Before diving into training, your first step is to gather and preprocess your data. Vertex AI works well with datasets stored in Google Cloud Storage or BigQuery.
Recommended Data Sources:
- Structured data: BigQuery tables (for tabular datasets)
- Unstructured data: Cloud Storage (for images, text, audio, etc.)
Use Dataflow or Cloud Dataprep to clean and transform data before loading it into VertexAI.
Step 2: Choose Between AutoML and Custom Model Training
Vertex AI supports two primary methods for training models:
A. AutoML (Code-Free Training)
AutoML is ideal for business users, analysts, or anyone who wants to build models quickly without writing complex code.
Steps:
- Go to the VertexAI dashboard
- Select “Create Dataset” and upload your data
- Choose your problem type (e.g., classification, regression, image analysis)
- Click “Train New Model”
- Let the system automate the training, tuning, and evaluation
You’ll receive a fully trained model with performance metrics and ready-to-deploy status.
B. Custom Model Training (For Developers)
If you’re using your own ML code (e.g., in TensorFlow or PyTorch), you can use custom training jobs on Vertex AI.
Steps:
- Write your training script (
train.py) - Store the script in Cloud Storage or a GitHub repo
- Launch the training job via:
- Vertex AI dashboard
gcloudCLI- Vertex AI Python SDK
Sample Training Job CLI Command:
bashCopy
Edit
gcloud ai custom-jobs create \ --region=us-central1 \ --display-name=my-model-training \ --python-package-uris=gs://your-bucket/code/train.tar.gz \ --python-module=train \ --machine-type=n1-standard-4 \ --args="--learning_rate=0.01","--epochs=10"
This method gives you full control over model architecture, parameters, and resource allocation.
Step 3: Evaluate Model Performance
After training, it’s time to review how well the model performed. Vertex AI provides interactive dashboards that show key evaluation metrics such as:
- Accuracy
- Precision and recall
- Mean absolute error (for regression)
- Confusion matrix (for classification)
You can compare multiple versions of your model and select the best one for deployment.
Step 4: Deploying the Trained Model
Once you’re confident in your model’s performance, the next step is deployment.
Deployment Process:
- Go to the “Models” section
- Select the model version you want to deploy
- Click “Deploy to Endpoint”
- Choose the machine type (e.g., n1-standard-2 or GPU-enabled instance)
- Set scaling options if needed
- Vertex AI automatically creates a REST endpoint
You can now use this endpoint to make real-time predictions from your web app, mobile app, or backend system.
Comparison Table: Vertex AI vs Other ML Platforms
| Feature | Vertex AI | AWS SageMaker | Azure ML Studio |
|---|---|---|---|
| AutoML Capabilities | Yes | Yes | Yes |
| Custom Training Support | Strong | Strong | Moderate |
| Built-in Monitoring | Integrated | Limited | Moderate |
| Google Services Integration | Deep | Limited | Limited |
| Real-Time Inference | Available | Available | Available |
VertexAI stands out for its tight integration with Google Cloud tools like BigQuery, Cloud Storage, and Dataflow—making it ideal for full-stack AI projects.
Step 5: Monitor and Manage Deployed Models
After deployment, VertexAI doesn’t leave you hanging. It includes features for:
- Model Monitoring: Track prediction quality over time
- Explainability: Understand why the model makes certain decisions
- Version Control: Roll back to earlier versions if needed
- Logging: View usage stats, errors, and latency
Use Case Example: Predicting Customer Churn
Let’s say a SaaS company wants to predict which customers are likely to cancel subscriptions. Here’s how they can use VertexAI:
- Load customer data into BigQuery
- Use AutoML Tables to train a classification model
- Deploy the model via Vertex AI
- Integrate the REST endpoint into their CRM
- Monitor predictions and retrain the model quarterly
This workflow can reduce churn by proactively reaching out to at-risk customers.
FAQs About Vertex AI
1. What is Vertex AI used for?
A. Vertex AI is used for building, training, deploying, and managing machine learning models on Google Cloud. It supports both code-free and custom workflows.
2. Is Vertex AI good for beginners?
A. Yes. With AutoML, even non-technical users can create models using an intuitive interface.
3. What programming languages does Vertex AI support?
A. Vertex AI supports Python-based frameworks like TensorFlow, PyTorch, and scikit-learn.
4.Can I scale my model after deployment?
A. Absolutely. Vertex AI allows autoscaling, manual scaling, and GPU integration depending on your workload needs.
Why Choose Vertex AI?
The modern AI landscape demands platforms that are scalable, integrated, and developer friendly. VertexAI delivers all of that and more. Whether you’re training models with minimal code or deploying advanced neural networks in production, Vertex AI gives you the tools to succeed.
Its ability to unify data, training, deployment, and monitoring under one roof makes it a powerful solution for organizations of all sizes. If you’re serious about deploying intelligent systems at scale, Vertex is a tool worth mastering.
