The new platform helps retailers better manage inventory, purchasing and other business critical functions.

This week Google announced the general availability of Vertex AI, a managed machine learning (ML) platform that allows companies to accelerate the deployment and maintenance of artificial intelligence (AI) models.

Craig Wiley, Director, Vertex AI, explained the new release in a blog post. “Today, data scientists grapple with the challenge of manually piecing together ML point solutions,” he explained. This creates a lag time in model development and experimentation, resulting in very few models making it into production.

“To tackle these challenges, Vertex AI brings together the Google Cloud services for building ML under one unified UI and API,” Wiley said. The aim here is “to simplify the process of building, training, and deploying machine learning models at scale.”

A single environment but with multiple features and modules

In this single environment, Google customers can move models from experimentation to production faster, he added. They can also more efficiently discover patterns and anomalies. They can also make better predictions and decisions, and generally be more agile in the face of shifting market dynamics.

With Vertex AI, data science and ML engineering teams can access the AI toolkit used internally to power Google. This includes computer vision, language, conversation and structured data, continuously enhanced by Google Research.

They can also deploy more, useful AI applications, faster with new MLOps features like Vertex Vizier. This increases the rate of experimentation. The Vertex Feature Store helps practitioners serve, share, and reuse ML features, and Vertex Experiments accelerates the deployment of models into production with faster model selection. In addition, Vertex ML Edge Manager helps deploy and monitor models on the edge with automated processes and flexible APIs.

The new Vertex platform removes the complexity of self-service model maintenance and repeatability. It does with MLOps tools like Vertex Model Monitoring, Vertex ML Metadata and Vertex Pipelines. These tools streamline the end-to-end ML workflow, Wiley said.