Google Vertex AI Is Now Generally Available

Google claimed that Vertex AI needs approximately 80% fewer lines of code to train a model as compared to other competitive platforms.

Recently, at Google I/O, the company announced the GA release of Vertex AI, which is a managed machine learning platform that helps companies to accelerate the deployment and maintenance of AI models.

Google claimed that Vertex AI requires approximately 80% fewer lines of code to train a model as compared to other competitive platforms. Vertex AI enables data scientists across all levels of expertise the ability to implement MLOps to efficiently build and manage ML projects throughout the entire development lifecycle. 

Vertex AI platform brings together the Google Cloud services for building ML under one unified UI and API, in order to simplify the process of building, training, and deploying ML models at scale. According to Google, this single environment enables users to move models from experimentation to production faster, more efficiently discover patterns and anomalies, make better predictions and decisions.

Source: Google

Google said that now, with the GA release of Vertex AI, ML engineering teams can: access the AI toolkit used internally to power Google; deploy more, useful AI applications, faster; manage models with confidence.

Here, AI toolkit used internally to power Google includes computer vision, language, conversation and structured data, continuously enhanced by Google Research.

New MLOps features like Vertex Vizier increases the rate of experimentation; the fully managed 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.

Vertex AI gives our data scientists the ability to quickly create new models based on the change in environment while also letting our developers and data analysts maintain models in order to scale and innovate. The MLOps capabilities in Vertex AI mean we can stay ahead of our clients’ expectations.”  said said Mark Bulling, SVP, Product Innovation at Essence.

To learn more you can check out Google's ML on GCP best practices, and this practitioners guide to MLOps whitepaper,