vertex ai

An Overview of Vertex AI

February 14, 2022

With machine learning on the rise, more and more data scientists are beginning to build their own models. People who have no experience with the industry have also gained interest, and they're wanting to learn as much as possible. With as much simplicity as Google Cloud services have brought to the industry, it is aimed to pare things down further with Vertex AI. Whether you work with machine learning (ML) models or you're just starting out, Vertex AI was built with you in mind.

So, what is this console? What does Vertex AI do? We're here to answer all your questions and more. (If you'd prefer a free 30 minute discovery call with our team, contact us)

Read on to learn everything you need to know about Vertex AI.

What Is Vertex AI?

Put simply: Vertex AI is a managed ML platform that places all of Google's Cloud services onto one dashboard. Google places a focus on maintaining the services so your workflow is never interrupted, and its automation makes it simple to learn.

It was built to target new users and experts alike, making it easy for people to learn while simplifying daily life for experts. According to Google, Vertex AI requires 80 percent fewer lines of code to train new ML models, making it simpler to deploy new programs.

How It's Used in a Machine Learning Model

Vertex AI works to provide tools for every step of machine learning development, and it's meant to optimize normal workflows.

So, here's what a typical workflow looks like, and then what Vertex AI has to offer.

1. Identify

First, you start with identifying the data you're looking to collect and how you're going to collect it. This first stage is one of the most important for machine learning, as you're defining the usefulness and accuracy of your overall project. What you collect here defines the quality of your entire ML machine.

In Vertex AI, however, identifying and analyzing are all done through managed datasets. There are tools that allow you to import data directly into the AI, and you can also annotate the data within the console.

That means you don't have to track down your own open-source datasets or write any lines of code to bring it all together.

2. Train

Once you've collected all your data, you move to pre-processing, creating, and training it to fit your ML model. Typically, you feed it all into your algorithm while it learns to appropriate parameters and features.

Once that's done, you move to refine your model using the validation dataset by modifying or discarding certain variables until you reach an acceptable level of accuracy.

With Vertex AI, you're going to have two options for this: auto and custom ML.

It's best to use auto for things like images, videos, other media, text files, and even tabular data. In these instances, you don't have to worry about writing model code — Vertex AI does it for you.

For more control over your model's architecture and framework, you can turn to custom ML. Here, you do have to write code, but you can tweak freely it and get as specific as you'd like.

3. Evaluate

The next step in a typical ML workflow is to check for efficiency, then deploy in order to make predictions. Usually, you'll use your test dataset to verify that your model is using accurate features.

Upon evaluation, you may either return to the drawing board to rework your algorithm, or you'll move to deploy the model.

While you still have to take these steps with Vertex AI, the program simplifies this process in one central dashboard. It also offers explainable AI, which allows you to dive deeper and analyze what factors are playing the largest role within your model.

You can customize those factors to fit into the model further, or you can work with them as they are.

4. Deploy

Finally, you move to deploy your model. The simplest way to accomplish this is through creating a web service for prediction. With Vertex AI, you can do it right inside the console.

The AI also allows you to do things like scale the model for lower latency if necessary for online use. You can even get your predictions through the command-line interface, console UI, or SDK and APIs.


With Vertex AI, you only pay for what you use, so you're going to save time and money for your infrastructure. It's meant to help reduce the costs of setting up your own ML model, and it has a significant impact on the effort you put in.

This is also where the AI targets beginners because even those with minimal experience are able to assist with tasks that might normally be reserved for the experts. As those beginners move forward with the AI, they'll be able to solve increasingly complex tasks and gain the experience needed to even move away from the platform and build independently build ML models.

Finally, the AI also works to reduce the risk of faulty production deployment.

Is Vertex AI Right for You?

Now that we've gone over Vertex AI, it's time to make your choice. While the platform is built for novices and experts alike, some are going to have varying opinions on its overall effectiveness. It's important to remember that, whether the platform works for you or not, it's okay to still try new things until you find the proper workflow for you.

No matter what program you're looking to start, or what data you need to collect, we're here to help. Contact us to get started on your ML model, ask questions or explore a potential project with our team. A 30-minute discovery call is free.

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