Google Makes Vertex AI Generally Available to Streamline Machine Learning Workflows
Google launches Vertex AI as a fully managed platform that unifies the machine learning lifecycle into a single interface to help data scientists build and deploy models faster.
Google announces the general availability of Vertex AI, a managed machine learning platform designed to simplify the development and maintenance of AI models. By unifying various Google Cloud services into a single user interface and API, the platform eliminates the need for data scientists to manually stitch together disparate point solutions. This consolidation significantly reduces the lag in model creation and experimentation that developers typically face.
The platform enables developers to build machine learning pipelines for training and evaluating models using either Google Cloud algorithms or custom training code. Users can easily process image, video, text, and tabular data before deploying their trained models for online or batch use cases on scalable managed infrastructure. As a result, data scientists move models from experimentation to production much faster and uncover data irregularities more effectively.
Vertex AI provides access to the same internal AI tools that Google uses, covering computer vision, language, conversation, and structured data applications. It also includes prebuilt containers for popular frameworks like TensorFlow and Scikit-learn, alongside tools like Vertex Vizier for faster experimentation and a fully managed Feature Store for reusing machine learning features. Additionally, an upcoming Vertex ML Edge Manager allows users to build and monitor models directly on edge devices when data must remain on-site.