Google Cloud announced the general availability of Vertex AI, a managed machine learning platform that allows companies to accelerate the implementation and maintenance of artificial intelligence models.

Vertex AI, highlighted Google Cloud, requires almost 80% less code lines for training a model than other platforms. This allows data scientists and machine learning engineers of all levels of competence to implement Machine Learning Operations (MLOps) to efficiently build and manage artificial intelligence projects throughout the life cycle of the

Vertex AI brings together Google Cloud services for machine learning construction under a single user interface and API, to simplify the process of building, training and deploying machine learning models on a scale.

In this single environment, Google Cloud said, customers can move models from experimentation to production faster, discover patterns and anomalies more efficiently, make better forecasts and decisions and in general be more agile in the face of changing dynamics of the

With Vertex AI, data science and machine learning teams can access the AI toolkit used internally to enhance Google, which includes computer vision, language, conversation and structured data, continuously enhanced by Google Research.

They can also distribute multiple applications of artificial intelligence and faster, with new MLOps features such as Vertex Vizier, which increases the rate of experimentation, the Vertex Feature Store fully managed, to help professionals provide, share and reuse the functionality of but

If data is to remain on the device or on site, Vertex ML Edge Manager (currently under trial) has been designed to distribute and monitor models on the edge with automated processes and flexible APIs.

Google Cloud proposes Vertex AI as a unique platform that contains all the necessary tools and that allows you to manage data, prototype, experiment, distribute, interpret and monitor models in production without requiring formal training in machine learning.

That is, Google Cloud points out, the organization’s data scientists do not need to be machine learning engineers to be operational. With Vertex AI, they have the ability to move quickly, but at the same time with a safety net that helps them launch their work.

The platform assists in responsible implementation and ensures a faster transition from testing and model management to production and finally to business results.

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