What is Vertex AI? How to Use It


Machine learning has revolutionized the way companies analyze data, make predictions and automate processes. However, for many, getting started with machine learning can be a daunting task. That’s where Google Cloud’s Vertex AI comes into play. Vertex AI is a comprehensive machine learning platform that simplifies the end-to-end process of building, training, and deploying machine learning models. In this article, we explore what Vertex AI is and how you can use it to harness the power of machine learning for your projects.

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What is Vertex AI?

Vertex AI is a machine learning (ML) platform developed by Google Cloud that provides an end-to-end workflow for building, training, and deploying machine learning models. It combines data engineering, data science and ML engineering workflows, allowing teams to collaborate using a common toolset and scale applications using the benefits of Google Cloud. Vertex AI offers several options for model training and deployment, including AutoML, which allows you to train table, image, text, or video data without writing code or preparing data splits.

How to use Vertex AI

Now that we understand what Vertex AI is, let’s see how we can use it effectively for your machine learning projects. The process can be broken down into several key steps:

Data preparation

Data extraction and cleaning

Start by extracting the data relevant to your machine learning task. This data can be saved in various formats such as CSV files, databases or cloud storage. After extraction, it is essential to clean the data and remove any inconsistencies or outliers.

Exploratory Data Analysis (EDA)

EDA is a crucial step in understanding the characteristics and distribution of your data. You can use Vertex AI tools to visualize and analyze your data set. EDA helps identify potential issues and insights that will guide the design of your machine learning model.

Data transformation and feature engineering

Perform data transformations and feature engineering based on your EDA results. This includes tasks like one-hot coding, scaling features, and creating new features from existing data to make it more suitable for machine learning algorithms.

Split data: Divide your data set into three main parts

training, validation and test sets. The training set is used to train your machine learning model, the validation set helps you tune hyperparameters, and the test set evaluates the model’s performance.

Model training

Vertex AI simplifies the model training process and offers several options, including AutoML. AutoML is especially useful for those who want to train models without writing code or preparing data splits. Here’s how to train your model with Vertex AI:

Select your training algorithm

Vertex AI offers a range of pre-built machine learning algorithms, including neural networks, decision trees and more. You can choose the one that best suits your task.

Configure hyper parameters

Set the hyperparameters for your model. Vertex AI can automate this process, but you can also fine-tune them manually for better results.

Start training

Once you have configured your model, start the training process. Vertex AI takes care of the heavy lifting so you can focus on the overall strategy.

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Model implementation

Once you’ve trained your machine learning model, it’s time to deploy it to make predictions on new data. Vertex AI simplifies the implementation process and here’s how you can do it:

Deploy to an endpoint

Choose an endpoint where you want to deploy your model. This can be a REST API endpoint, allowing you to easily integrate your model with different applications and services.

Retraining for better performance

Vertex AI allows you to retrain your model using incoming prediction data. This continuous learning process ensures that your model adapts to new patterns and maintains high accuracy.

Model monitoring

Once your model is deployed, it is essential that you monitor its performance. Vertex AI offers a feature called Model Monitoring that allows you to track how well your model performs in real-world scenarios. Here’s how to do it:

Configure monitoring

Set monitoring parameters such as data drift, model drift, and data quality thresholds. Vertex AI will continuously evaluate your model against these metrics.

Retrain for better performance

Just like during deployment, you can use incoming prediction data to retrain your model for better performance. This ensures that your model remains effective as data patterns change over time.

Frequently Asked Questions (FAQs)

1. Is Vertex AI suitable for machine learning beginners?

Absolute! Vertex AI offers easy-to-use tools, including AutoML, that allow even beginners to train machine learning models without extensive coding knowledge.

2. Can I use Vertex AI for image and video data?

Yes, Vertex AI supports several data types, including table, image, text, and video data. You can build models for a wide range of applications.

3. What are the costs associated with using Vertex AI?

Pricing for Vertex AI varies based on factors such as amount of training and prediction usage. Google Cloud provides detailed pricing information on their website.

4. Can I use Vertex AI for custom machine learning models?

Yes, Google Cloud offers several tools and services for deploying custom machine learning models. You can use Vertex AI or other Google Cloud services such as AI Platform to deploy your own custom models.

5. Is there a trial or free tier for Vertex AI?

Google Cloud typically offers a free trial with a certain amount of credit that can be used to explore and experiment with different services, including Vertex AI. Make sure to visit Google Cloud’s official website for the most up-to-date information on their trial offers.


Google Cloud’s Vertex AI is a powerful machine learning platform that simplifies the process of building, training, and deploying machine learning models. Whether you’re new to machine learning or an experienced data scientist, Vertex AI provides the tools and infrastructure to accelerate your projects. With features like AutoML, model monitoring, and support for different data types, Vertex AI enables companies to make effective data-driven decisions and predictions.

By following the steps in this article, you can unlock the full potential of Vertex AI and leverage the benefits of Google Cloud for your machine learning efforts. Whether you’re extracting insights from structured data or working with complex images and videos, Vertex AI has the solution for you. Get started today and start your machine learning journey with Vertex AI from Google Cloud.

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