There are so many artificial intelligence (AI) tools out there, that it’s hard to figure out which one you truly need. We can help make this decision easier by introducing you to Vertex AI, Google’s very own AI cloud-based platform. 

The recent boom in AI models and technological advances reshapes how many businesses operate; the only issue is that there are too many solutions to choose from. But we can help. 

To make it easier to build machine learning (ML) models, Google created Vertex AI, a platform that makes all of Google’s cloud offerings available in a single entity.

But what is Vertex AI, in essence? Is it worthy of your attention or is it just a flop? Discover the answer in this guide, as we’ll explore why Vertex AI is so important to businesses and many other things.

Stay tuned!

What is Vertex AI?

Vertex AI is a centralized platform created by Google Cloud that aims to train ML models and AI applications and deploy them at scale. Moreover, you can customize large language models (LLMs) to use in your AI applications. 

Through features like custom model training, AutoML, and other advanced capabilities, you can greatly boost productivity and scalability. 

Plus, Vertex AI provides all of Google’s cloud services in a single tool, helping you with your data, deploying models, and more. In a nutshell, Vertex AI is here to streamline your ML workflow and simplify your dataset lifecycle. 

It’s a solution ideal for all developers and ML engineers, especially since it’s so beginner-friendly. 

But why is this tool so important for businesses? 

Why is Vertex AI Important?

The generative AI market is constantly growing. For instance, the number of active gen AI projects on Vertex AI has grown more than 7x; but what exactly makes Vertex so important?

Well, let’s see what it does: 

• Streamlined development: This AI solution automates the entire development process. So, anyone — regardless of their experience with such tools — can deploy high-quality models. 
• Higher speed: Since you automate the model development process, you’ll train and deploy your chatbots faster. As a result, it’s easier and quicker to create AI apps. 
• Consistency: AIs are good at one thing: maintaining consistency across the board. Aside from having the same quality output, you’ll also minimize errors.
• Scalability: You can design all kinds of AI apps with Vertex, from small-scale applications to enterprise-level solutions. No matter the scale of your organization, it can fit every need. 

As you can see, Vertex AI clearly provides plenty of benefits but what are the AI features that help it become so great? 

Find the answer in the next section. 

What are the Google Cloud Vertex AI Key Features?

To understand what makes this Google Cloud platform so good at deploying and building AI models, let’s look at its features: 

• AutoML: This functionality is for developers who don’t have a lot of experience with ML, but want to build their own custom ML models. It works for various Google apps, including Vertex AI, AutoML Tabular, and AutoML Image. 
• Vertex AI pipelines: With this handy feature, you can automate, monitor, and create entire ML workflows that boost your efficiency and save you from repetitive tasks. 
• Pre-trained APIs: With Vertex AI, integrating videos, translation, and natural language processing is easy. As a result, the deployed models will meet business requirements even if the engineers and developers have no prior knowledge. 
• Data and AI integration: Vertex AI plays nicely with plenty of tools, like BigQuery ML, Dataproc, or Dataflow. So, you can build or export data from BigQuery directly through the Vertex AI Workbench. Moreover, it integrates with open-source frameworks like PyTorch and SensorFlow. 
• Model versioning and monitoring: Lost track of your versions? With Vertex AI, that’ll never happen again as it easily keeps track of the model’s versions. Plus, Vertex AI monitors their performance through useful evaluation metrics so you can proactively identify issues. 

So, Vertex AI has plenty of user capabilities; the only issue is, what are you gonna use them for?

That’s a question we’ll try to answer in the following section. 

What is Vertex AI Used For? 

Usually, people use artificial intelligence tools in a wide range of areas, the most popular being in customer service (56%) and fraud management (51%). But it has plenty of other uses, such as product recommendations, content production, and others. 

Going by this logic, is Vertex AI good at something? It sure is; here are its most common use cases: 

• Improved forecasting: Use Vertex AI to improve the accuracy of your real-time predictions, as this tool can analyze and interpret metadata easily and at speed. 
• Streamlined monitoring: Vertex AI monitors model performance, from inception to final product, so you can streamline the process and get better results. 
• Shared ML features: Through the Vertex AI Feature Store, you can manage and reuse the ML features you need. What’s more, you don’t need in-depth ML knowledge to use them. 
• Enhanced development process: With Vertex AI, it’s simple to streamline, configure, and automate ML workflows so you can speed up the development process. 

As you can see, Vertex AI has multiple use cases that make it a perfect addition to your business.

Before we learn how to use it, let’s take a look at the Google Cloud services that mesh well with Vertex AI. 

What Google Cloud Services Work in Sync with Vertex AI?

Vertex AI never works alone — it’s in sync with other Google Cloud services that help streamline your ML lifecycle and enhance your efficiency. But what are Google Cloud services?

In essence, it’s a toolkit of cloud computing services, like data analysis, cloud storage, networking, serverless computing, web development tools, etc. All these solutions work in sync with Vertex AI to create efficient ML workflows.

For instance, in Google’s cloud storage, you can store all your raw data that Vertex can later access to train models. BigQuery is a similar tool that stores large datasets that Vertex AI uses for in-suite training. 

As you can see, everything is connected with Vertex AI to help you create and deploy the best AI solutions. 

One thing is left to discuss: how to actually use Vertex AI to start building. 

How to Use Vertex AI and Machine Learning (ML) Workflow

You’re curious about using Vertex AI to streamline your ML workflows — that’s great! Let’s show you how easy it is step-by-step: 

1. Data preparation

Firstly, start by extracting and cleaning your dataset. 

After you do this, you must perform an exploratory data analysis (EDA) — a technique that summarizes the main characteristics of data sets. Why? To understand the data expected by the ML model. Visualize and access your data using the Vertex AI Workbench. 

What comes next? Applying data transformations and feature engineering to the model, as well as separating the data into training, validation, and test sets. 

2. Model training

Now comes the fun part. Here’s where data scientists, developers, and other people can choose a training method in tune with their business needs. You can train your ML models in various ways: 

• Using AutoML overview, which trains your models without writing any code. It supports image, text, and tabular data.
• Using your favorite ML framework, which means writing your own code and training your models by yourself.
• Using Vertex AI Experiments, which tests your models using various ML techniques and seeing which one performs best. 
• Using Vertex AI Vizier or custom tuning jobs, which focuses on hyperparameter tuning to train complex custom ML models. 

3. Model evaluation and iteration

The third step requires you to evaluate your model’s performance and make adjustments based on the results. This helps you actively find issues and solve them before deploying the model.

Through metrics such as precision and recall, you can compare and measure the performance of your models, and see which one performs best overall. 

4. Model serving

After carefully evaluating and okaying your model’s performance, it’s time to deploy it. 

Again, you have various methods for doing so: 

• Using pre-built or custom containers 
• Using BigQuery ML
• Using TensorFlow runtime 
• Using Vertex AI Feature Store  
• Using Vertex Explainable AI 

5. Model monitoring

You’re not done yet, as there’s always room for improvement. The final step in any model deployment process is to monitor its performance. With Vertex AI Model Monitoring, you can keep an eye on your models and improve their performance if the data strays too far from their training. Moreover, Vertex AI has alerts and notifications that let you know when something is wrong, so you can retrain your model to perform up to standard. 

With this quick and simple tutorial, you’re ready to try Vertex AI.

Over To You

With Vertex AI, it’s easy to manage ML workflows and ensure their efficiency, accuracy, and consistency. Now that you know the answer to, “What is Vertex AI?” you’re one step closer to streamlining your business processes.

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