Let’s briefly walk through what this means—without getting into details yet.

• First, data lives in Cloud Storage. This could be a CSV file, a set of images, or any other dataset you want to use for training. Cloud Storage is the starting point of the pipeline and acts as a central place for your ML data.
• Next, Vertex AI uses that data to train a model. Training is the process of teaching a model to recognize patterns in the data. GCP handles the servers, scaling, and infrastructure so you can focus on the task itself.
• Once training is complete, the trained model is saved and versioned in the Vertex AI Model Registry. This is important because models change over time, just like application code. Keeping track of versions allows you to update or roll back models safely.
• After that, the model is deployed to a Vertex AI Endpoint. Deployment turns the model into a web-accessible service. Instead of being just a file, the model can now receive requests and return predictions.
• Finally, applications or users send data to the endpoint and receive predictions in response. This is where machine learning becomes truly useful—when it’s integrated into real-world systems, such as web apps, mobile apps, or backend services.

At this stage, you don’t need to understand how each service works internally. What matters is recognizing that machine learning on GCP is a connected pipeline, where each resource plays a clear role.

In the next sections, we’ll break down each step of this pipeline one by one, starting with how and where your data is stored.

Storing Your Data

Every machine learning pipeline starts with data. Think of data as the “ingredients” you’ll use to train your model. Without good data, even the most innovative model won’t make accurate predictions.

On GCP, the service that handles this is Cloud Storage.

What is Cloud Storage?

Cloud Storage is Google Cloud’s place to store files in the cloud. You can think of it like Google Drive, but designed to hold datasets, model files, or anything else you need for ML. It’s secure, scalable, and accessible from other GCP services, such as Vertex AI.

How Data Fits Into the Pipeline

1. Upload your dataset: This can be a CSV file containing exam scores, a folder of images, or any other structured data.
2. Organize files in buckets: A bucket is similar to a folder in Cloud Storage, holding your datasets. You can create separate buckets for training data, test data, or model outputs.
3. Version control (optional but helpful): Cloud Storage can keep previous versions of your files. This is handy if you need to roll back to an earlier dataset.

Why This Matters for ML

• ML models can’t train themselves; they need data.
• Cloud Storage is the central place where all your ML pipeline services get the data they need.
• It keeps your datasets safe, accessible, and ready for training.

Training a Model

Once your data is safely stored in Cloud Storage, the next step in the ML pipeline is training a model.

Training is the process by which a machine learning model learns patterns from your data so it can make predictions later. Don’t worry—you don’t need to understand complex algorithms to get the big picture.

Vertex AI: The Beginner-Friendly Training Tool

On GCP, the service that handles training is Vertex AI. Vertex AI is a fully managed service, which means GCP handles all the servers, scaling, and infrastructure for you. You need to provide your data and select some basic settings.

There are two beginner-friendly ways to train models on Vertex AI:

1. AutoML (No Code Needed)
   • Vertex AI analyzes your dataset automatically.
   • It chooses the correct model type for your data.
   • You don’t need to write any Python or machine learning code.
2. Managed Training (Basic Customization)
   • If you have some Python or code, you can bring your own model.
   • Vertex AI handles the training infrastructure, so you don’t have to manage servers or GPUs yourself.

What Happens During Training?

• The model reads the data from Cloud Storage.
• It finds patterns or relationships in the data.
• The model’s “knowledge“ is saved as a file that can be deployed later.

Saving and Versioning the Model

After your model is trained in Vertex AI, the next step is to save it and keep track of its versions. This ensures you can reuse your models, improve them over time, and avoid mistakes from overwriting important work.

Vertex AI Model Registry: Your Model Library

GCP provides the Vertex AI Model Registry, a service that acts like a library for all your trained models. Each time you train a new model, you can save it here, along with metadata like:

• Model name
• Version number
• Training date
• Metrics (how well the model performed)

This way, you always know which version is the best, and you can roll back to an older model if needed.

Why Versioning Matters

• Track improvements: As you retrain models with new data, you can compare results.
• Avoid mistakes: Accidentally overwriting a model can ruin your predictions.
• Simplify deployment: Each version can be deployed independently to test in a production environment.

Cloud Storage Backup

While Vertex AI handles versioning, it’s also a good idea to store a copy of your model in Cloud Storage. This serves as a safe backup, making it easy to transfer models between projects or environments.

Deploying the Model

Once your model is trained and saved, it’s time to deploy it. Deployment is the step that transforms a static model file into a functioning service that applications can utilize to make predictions.

Vertex AI Endpoint: Making Your Model Accessible

On GCP, the service used for deployment is the Vertex AI Endpoint. Think of it as a web address (URL) for your model. Once deployed, the model can receive data from applications and automatically return predictions.

Key points about deployment:

• Vertex AI handles all the infrastructure for you—you don’t need to manage servers.
• The endpoint can scale automatically, so it can handle more requests as required.
• You can deploy different versions of a model to test and compare performance.

Why Deployment Matters

• It’s the point where machine learning becomes useful in real life.
• Applications (web apps, mobile apps, or backend systems) can now send data and receive predictions.
• Proper deployment ensures your model is fast, reliable, and secure.

Quick Beginner Checklist

When deploying a model for the first time:

1. Choose the Vertex AI Endpoint option.
2. Select the model version you want to deploy.
3. Confirm the deployment—Vertex AI handles the rest.
4. Note the endpoint URL to connect your applications.

Making Predictions

Now that your model is deployed to a Vertex AI Endpoint, it’s ready to do what it was trained to do: make predictions.

How Predictions Work

Making predictions is simply sending new data to the deployed model and receiving an output. For example:

• You trained a model to predict whether a student will pass an exam.
• You send a new student’s scores to the model.
• The model returns a prediction: pass or fail.

The model does not retrain during this step—it only applies what it learned during training.

GCP Resource: HTTPS Endpoint

The Vertex AI Endpoint provides a secure HTTPS URL that your applications can use to interact with the model.

• Web apps: Send requests from the browser or backend server
• Mobile apps: Send user data from a phone app
• Backend systems: Integrate predictions into workflows or dashboards

All requests and responses are handled automatically, so you don’t have to manage servers or scale manually.

Securing the Pipeline

Once your ML model is trained, saved, deployed, and making predictions, it’s important to control who can access it. Security ensures that only authorized users or applications can interact with your data or model.

GCP Resource: IAM and Service Accounts

On GCP, access control is managed with Identity and Access Management (IAM) and Service Accounts:

• IAM: Lets you assign roles to users or groups. For example, you can allow someone to upload data, but not deploy models.
• Service Accounts: Special accounts used by applications to securely interact with GCP services without using personal credentials.

Beginner-Friendly Rules

1. Principle of Least Privilege: Give only the necessary.
   • Example: An app that requests predictions should not be able to delete your training data.
2. Separate roles for training and deployment:
   • Training team → access to Cloud Storage and Vertex AI Training
   • Deployment team → access to Vertex AI Endpoint
3. Monitor access: Regularly check who has permissions to your resources.

Monitoring and Cost Awareness

Once your ML pipeline is running—data stored, model trained, deployed, and serving predictions—it’s essential to monitor its usage and manage costs. Even simple pipelines can incur charges if endpoints or training jobs are left running.

GCP Resources for Monitoring

1. Cloud Monitoring
   • Let’s acknowledge resource usage, such as how many prediction requests your model receives or how much compute was used during training.
   • Provides charts, alerts, and logs to help you spot issues.
2. Budgets and Alerts
   • You can set a budget in GCP and receive alerts when spending approaches a limit.
   • Helps beginners avoid surprises in billing.

Why This Matters

• Models cost money to run: Deployed endpoints and large training jobs use compute resources that are billed.
• Prevent waste: Stopping unused endpoints or deleting outdated models saves money.
• Detect problems early: If predictions drop or usage spikes unexpectedly, monitoring helps identify the issue.

Summary

In this article, we walked through a beginner-friendly machine learning pipeline on Google Cloud Platform (GCP). By following these steps, you can understand how data turns into predictions using cloud resources—even without any coding or advanced ML knowledge.

Here’s a recap of the pipeline steps:

1. Store data – Keep your datasets safe and organized using Cloud Storage.
2. Train model – Teach the model to recognize patterns in your data using Vertex AI.
3. Save & version model – Store models and track different versions with the Vertex AI Model Registry.
4. Deploy model – Turn the trained model into a web-accessible service via a Vertex AI Endpoint.
5. Make predictions – Let applications send data and get predictions using the model’s endpoint.
6. Secure pipeline – Control who can access data and models with IAM and Service Accounts.
7. Monitor usage & costs – Track performance and prevent unnecessary spending using Cloud Monitoring and budgets.

Key Takeaways for Beginners

• ML pipelines are simple sequences of steps: data → training → saving → deployment → predictions.
• GCP resources handle the heavy lifting, so you don’t have to manage servers or worry about scaling.
• Security and monitoring are essential, even for beginner pipelines.
• Understanding this workflow gives you a solid foundation for learning more advanced ML and cloud practices later.

Next Steps

So far, we’ve created a basic ML pipeline on GCP: storing data, training a model, saving and versioning it, deploying it, making predictions, securing access, and monitoring usage.

This is everything a beginner needs to understand the core workflow. But in real-world projects, ML pipelines can become more complex. That’s where MLOps comes in.

What is MLOps?

MLOps (Machine Learning Operations) is the practice of automating and managing ML pipelines at scale. It combines ideas from software engineering, DevOps, and ML to make pipelines:

• Repeatable: Models can be retrained automatically as new data becomes available.
• Reliable: Pipelines detect and handle failures
• Scalable: Handle more data and users without manual intervention
• Monitored: Automatically track model performance and drift

Supplementary Resources

Vertex AI Tutorials & Hands‑On Guides — Step‑by‑step tutorials for training and deploying models on GCP.

GCP Machine Learning & AI Training Paths — Google official training courses and paths for ML & Vertex AI.

References

• • https://docs.cloud.google.com/vertex-ai/docs
  • https://docs.cloud.google.com/storage/docs
  • https://docs.cloud.google.com/iam/docs/overview
  • https://docs.cloud.google.com/monitoring/docs