Vertex AI Platform: Supercharge Your ML Now

The 2023 TechRadar by Devoteam report identifies the Vertex AI platform as a technology that businesses should adopt. But what is Vertex AI, and why should organisations consider rolling it out now?

What is Vertex AI and what is it used for?

Google describes the Vertex AI platform as a machine learning (ML) platform that allows you to train and deploy ML models and AI applications. Vertex AI combines data engineering, data science, and ML engineering workflows. This integration enables your teams to collaborate using a common toolset.

Simply put, Vertex AI provides a unified set of APIs for the ML lifecycle. For many businesses, having all the necessary tools to build a production-quality AI model in one place will be a powerful proposition.

Vertex AI includes tools and features to support the entire machine learning workflow:

Data Preparation
Model Training
Model Evaluation
Model Deployment
Model Monitoring
Model Explainability

Vertex AI also includes tools that identify gaps in your machine learning processes, offering opportunities to improve operations continuously.

What are the ideal use cases for Vertex AI?

As an all-in-one platform, the Vertex AI platform is a good choice for businesses that want to:

Productionise their machine learning workflows.
Consolidate ML and AI tools into a single platform.
Develop AI systems that are entirely infrastructure-managed.
Extend their existing investment in Google Cloud services.
Use pre-configured APIs to speed up development and deployment.
Evolve their use of AI over time from pre-configuration to complete customisation.

Thanks to its flexibility and the comprehensive nature of its AI/ML tool offerings, the Vertex AI platform suits most businesses and use cases.

Who uses Vertex AI?

According to a report published by TechCrunch, high-profile Vertex AI customers include Ford, Seagate, Wayfair, Cashapp, Cruise, and Lowe’s. These enterprises are obviously large, but the pay-as-you-use resource billing model makes Vertex AI a potential choice for smaller organisations too.

What are the top benefits of Vertex AI?

As mentioned earlier, the Vertex AI platform offers a powerful and flexible choice for machine learning and artificial intelligence operations (MLOps). Here are some specific benefits available to users:

Open Source support

Many businesses may already have AI models in operation, often built on open-source frameworks like PyTorch and TensorFlow. The Vertex AI platform supports these and other open-source frameworks, making it an ideal target for organisations wanting to move their models to the cloud.

One platform for all ML and AI tasks

Vertex AI consolidates all of Google’s machine learning and artificial intelligence tools into a single platform. This centralisation helps reduce costs and administrative overheads while allowing data science teams to collaborate more closely.

Fully managed infrastructure

Machine learning can be resource-intensive, requiring significant capital investment in infrastructure. Vertex AI allows you to scale resources based on demand, with charges according to usage. Meanwhile, Google manages, maintains, and optimises the platform, reducing workloads on your infrastructure team.

Faster go-to-market (GTM)

Google claims that the Vertex AI platform requires 80% less code than other ML platforms when training models. It also offers pre-trained APIs that help your teams accelerate development and focus on delivering value to end users.

MLOps ready

As artificial intelligence becomes a cornerstone for strategic computing, your internal operating processes will need to evolve. According to Google, the MLOps tools included with Vertex AI will help you collaborate across AI teams. These tools improve models through predictive model monitoring, alerting, diagnosis, and actionable explanations.

Is Vertex AI free?

No, the Vertex AI platform is not free. As a cloud-based platform, it bills users according to the compute resources and services used. Google provides comprehensive documentation outlining charges determined by the type and volume of data analysed, the type of analysis performed, and factors like CPU choice and physical storage type.