What’s New With Google Vertex AI?
August 12, 2022
Sundar Pichai introduced Vertex AI to the world during the Google I/O 2021 conference last year, placing it against managed AI platforms such as Amazon Web Services (AWS) and Azure in the global AI market.
The Alphabet CEO once said, “Machine learning is a core, transformative way by which we’re rethinking how we’re doing everything.”
A November 2020 study by Gartner predicted a near-20% growth rate for managed services like Vertex AI. Gartner said that as enterprises invest more in mobility and remote collaboration technologies and infrastructure, growth in the public cloud industry will be sustained through 2024.
Vertex AI replaces legacy services like AI Platform Training and Prediction, AI Platform Data Labeling, AutoML Natural Language, AutoML Vision, AutoML Video, AutoML Tables, and Deep Learning Containers. Let’s take a look at how the platform has fared and what’s changed over the last year.
What Is Google Vertex AI?
Google Vertex AI is a cloud-based third-party machine learning (ML) platform for deploying and maintaining artificial intelligence (AI) models. The machine learning operations (MLOps) platform blends automated machine learning (AutoML) and AI Platform into a unified application programming interface (API), client library, and user interface (UI).
Previously, data scientists had to run millions of datasets to train algorithms. But the Vertex technology stack does the heavy lifting now. It has the computing power to solve complex problems and easily do billions of iterations. Vertex also comes up with the best algorithms for specific needs.
Vertex AI uses a standard ML workflow consisting of stages like data collection, data preparation, training, evaluation, deployment, and prediction. Although Vertex AI has many features, we’ll look at some of its key features here.
Google Vertex AI: Feature Overview
- Whole ML Workflow Under a Unified UI Umbrella: Vertex AI comes with a unified UI and API for every Google Cloud service based on AI.
- Integrates With Common Open-Source Frameworks: Vertex AI blends easily with commonly used open-source frameworks like PyTorch and TensorFlow and supports other ML tools through custom containers.
- Access to Pretrained APIs for Different Datasets: Vertex AI makes it easy to integrate video, images, translation, and natural language processing (NLP) with existing applications. It empowers people with minimal expertise and effort to train ML models to meet their business needs.
- End-to-End Data and AI Integration: Vertex AI Workbench enables Vertex AI to integrate natively with Dataproc, Dataflow, and BigQuery. As a result, users can either develop or run ML models in BigQuery or export data from BigQuery and execute ML models from Vertex AI Workbench.
BY KASHYAP VYAS
JULY 26, 2022, IT Business edge
