2022-07-05

Vertex AI Overview

What is Vertex AI

Vertex AI is Google Cloud's end-to-end platform that offers a suite of tools and services to accelerate the development and deployment of machine learning models. By integrating essential components such as data processing, model training, and prediction services, Vertex AI eliminates the need for multiple tools and streamlines the AI development process. It is designed to support custom models, catering to various business use cases.

Vertex AI's Features

Vertex AI offers a diverse range of features designed to streamline the entire ML development process.

AutoML

AutoML is a feature within Vertex AI that automates the process of selecting the best ML model for a specific task. It automatically preprocesses data, selects the optimal model architecture, and tunes hyperparameters, making it easy for users with minimal ML expertise to build high-quality models for tasks such as image and text classification, object detection, and more.

Deep Learning VM Images

Deep Learning VM Images provide pre-built virtual machine (VM) images equipped with popular ML frameworks and tools, such as TensorFlow, PyTorch, and Jupyter. These VMs allow users to quickly set up a development environment for deep learning projects, reducing the time spent on configuring and managing the underlying infrastructure.

Vertex AI Workbench

Vertex AI Workbench is an integrated development environment (IDE) that facilitates collaboration between developers and data scientists. It offers a shared workspace for creating, experimenting, and collaborating on ML projects, providing seamless access to data, code, and computational resources. Users can develop and test models directly within the Workbench using supported ML frameworks.

Vertex AI Matching Engine

The Vertex AI Matching Engine is designed to help businesses implement recommendation systems. It enables users to create high-quality matching and ranking models using deep learning techniques, which can be used to provide personalized recommendations to customers across various industries, such as e-commerce, entertainment, and more.

Vertex AI Data Labeling

Vertex AI Data Labeling provides a service to create high-quality training data for ML models by annotating datasets with necessary labels. It supports various annotation types, such as image segmentation, text classification, and bounding box annotations. Users can also leverage Google's AI-assisted labeling to improve labeling efficiency and reduce manual effort.

Vertex AI Deep Learning Containers

Deep Learning Containers are pre-built, optimized container images that come with popular ML frameworks and tools, such as TensorFlow, PyTorch, and Scikit-learn. These containers enable users to develop, test, and deploy ML models quickly and efficiently across various environments, including local machines, cloud, and on-premises infrastructure.

Vertex Explainable AI

Vertex Explainable AI offers a suite of tools to help users understand, interpret, and explain their ML models' predictions. It provides feature attributions and model explanations, enabling users to gain insights into their models' decision-making processes, identify biases, and improve overall model quality.

Vertex AI Feature Store

The Feature Store is a central repository for storing, sharing, and managing features used in ML models. It enables users to create, discover, and reuse features across multiple models and projects, resulting in reduced duplication of effort and improved collaboration. The Feature Store also supports real-time and batch feature processing, catering to a variety of ML use cases.

Vertex ML Metadata

Vertex ML Metadata is a service that helps users track, manage, and analyze metadata generated throughout the ML development process. It enables users to maintain a comprehensive record of their ML experiments, datasets, and models, facilitating reproducibility and collaboration.

Vertex AI Model Monitoring

Model Monitoring provides a suite of tools to monitor the performance of deployed models in real-time. With integrated alerting and drift detection features, users can quickly identify and resolve issues related to model performance, ensuring accurate predictions and high-quality AI solutions.

Vertex AI Neural Architecture Search

Neural Architecture Search (NAS) is a technique used to automatically discover the optimal model architecture for a given task. Vertex AI NAS simplifies this process by providing an automated, scalable, and cost-effective solution to search for the best neural network architectures. By leveraging NAS, users can optimize their models for performance, accuracy, and efficiency without investing significant time and resources in manual experimentation.

Vertex AI Pipelines

Vertex AI Pipelines simplify the process of orchestrating and automating ML workflows. Users can create, execute, and manage complex ML workflows using a graphical interface or code. These pipelines are designed to be reproducible, scalable, and maintainable, ensuring a streamlined ML development process.

Vertex AI Prediction

Vertex AI Prediction enables users to deploy trained models for online or batch predictions. The platform handles the underlying infrastructure management, ensuring the optimal utilization of resources while maintaining low latency and high throughput. Users can also monitor and analyze the performance of their models in real time through integrated logging and monitoring tools.

Vertex AI TensorBoard

TensorBoard is a visualization tool that helps users analyze and debug ML models. Integrated within Vertex AI, TensorBoard allows users to visualize model training metrics, compare different runs, and track model performance over time. This powerful tool assists users in identifying issues and optimizing their models for better performance.

Vertex AI Training

Vertex AI Training is a managed training service that simplifies the process of training ML models. With support for custom and pre-built models, users can train models using various ML frameworks such as TensorFlow, PyTorch, and XGBoost. Vertex AI also provides distributed training capabilities, enabling users to leverage Google Cloud's powerful infrastructure to train models faster and at scale.

Vertex AI Vizier

Vertex AI Vizier is a hyperparameter optimization service that helps users find the best hyperparameter settings for their ML models. By using advanced optimization algorithms, Vizier automates the process of tuning hyperparameters, resulting in improved model performance and reduced experimentation time.