Data Backup Digest

With so much emphasis on artificial intelligence, particularly within the cloud, it was only a matter of time before we saw a fully managed, cloud-driven AI service – but that's exactly what Google's Vertex AI has to offer. In short, Vertex AI makes it easy for developers to deploy and maintain AI models on modern cloud infrastructure.

It achieves this through several different ways and methods, including:

- Empowering developers and giving the ability to build their apps with some of the same machine learning tools that drive Google itself.

- Making it possible to deploy more models than ever before, all with greater speed and efficiency than prior mechanisms. Additionally, 80% fewer lines of code required when building customized AI models.

- Using MLOps tools to manage data, maintain models, and scale as necessary.

Craig Wiley, product manager with Google Cloud AI, explained the reason for Vertex AI in a recent interview by saying: "Vertex was designed to help customers with four things. The first is, we want to help them increase the velocity of the machine learning models that they’re building and deploying. Number two is, we want to make sure that they have Google’s best-in-class capabilities available to them. Number three is, we want these workflows to be highly scalable. And then number four is, we want to make sure they have everything they need for appropriate model management and governance."

Additional Features

Vertex AI has some additional features, too. For example, there are specific features aimed at increasing the data experimentation rate, improving model selection, pipeline monitoring, and sharing new machine learning features amongst developers.

It's also fully integrated with Vizier – an AI optimizer that was also developed by Google – to help tune parameters and manage data in complex machine learning models. Not only does this reduce the amount of time that it ultimately takes to tune an AI model, but it also gives engineers the ability to run even more experiments in a less amount of time overall.
All of these features combine to create a comprehensive and fully managed cloud AI service.

Pre-Training

Google's Vertex AI has even been pre-trained in a variety of disciplines, including vision processing, video recognition, natural language, and more. These pre-made templates can easily be dropped into cloud-based apps to add even more functionality.

Moreover, Vertex AI includes an AutoML features that simplifies the process of machine learning and training. A centralized registry for all datasets and data types, like vision, natural language, and tabular, simplify these processes even further.

BigQuery ML

Vertex AI is also integrated directly with BigQuery ML, so you can create and execute models within BigQuery via standard queries. You can also export datasets from BigQuery into Vertex AI as needed. To track all of your data, Vertex Data Labeling makes it easy to create labels and assign them to various datasets in your repository.

For more information on {{https://cloud.google.com/vertex-ai|Google Vertex AI}}I, please visit their official website.