Google’s Vertex AI in line

Shruti Govil
Shruti Govil April 12, 2024
Updated 2024/04/12 at 4:54 PM

Vertex AI, a single artificial intelligence platform that provides access to all of Google’s cloud services, was introduced by the company approximately three years ago. Now that Gemini is working on BigQuery and Looker, users can access the AI functionalities. 

What is Vertex AI?

Google’s unified artificial intelligence (AI) platform, Vertex AI, is intended to tackle and resolve a variety of issues that arise during the development and application of machine learning (ML) models. This hosted machine learning system allows you to deploy and maintain AI models by providing you with single-point access to all of Google’s cloud services.


Tasks can be automated by a machine learning workflow, giving you more time to focus on more important work. It is known as a “managed” platform. With Vertex AI, Google hopes to speed up basic tasks for experts and simplify them for novices.


Vertex AI’s importance

Vertex AI’s managed platform is one of its main benefits; it streamlines numerous processes and makes basic jobs simpler and quicker for experts alike. This platform assists companies in streamlining their machine learning processes so they can concentrate on improving their models.

Users can benefit from automated model tuning and deployment features with Vertex AI, which decrease human work and increase productivity. To help getting started with machine learning easier, the platform also provides pre-built templates and code libraries.


Features of Vertex AI

Features that give businesses the resources they need to build, train, and implement successful machine learning models are included into Vertex AI.

  1. Vertex ML Edge: This tool helps decrease model size and speed up inference by providing features like hardware acceleration, optimization, and compression.
  2. Vertex Data Labeling: It provides users with a range of annotation tools, including polygons, bounding boxes, and semantic segmentation, to facilitate the labeling of complex information.
  3. Vertex Feature Store: It provides tools for versioning, access control, and monitoring that help teams ensure that their data is consistent and of high quality. 
  4. Vertex Experiments: Its capabilities, which include job scheduling, hyperparameter tuning, and simple experiment monitoring, enable businesses to easily fine-tune their machine learning models for peak performance.
  5. Vertex TensorBoard: Its functions, which include model comparison, real-time monitoring, and graph visualization, help users comprehend the behavior of their models. 
  6. Vertex ML Metadata: This tool makes sophisticated ML workflow management easier for businesses by providing capabilities like experiment tracking, artefact versioning, and lineage tracking.


Click to know about Google’s latest AI platform – Google Vertex AI

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