Monday, August 8, 2022

Google Cloud: Vertex AI Review

Google Cloud Vertex AI is an artificial intelligence platform that enables users to build and deploy machine learning (ML) models and scale them using a variety of pre-trained and customizable tools.

Vertex AI also hosts all of Google’s cloud services on the same platform.

See below to learn all about Google Cloud Vertex AI and where it stands the global AI sector:

The AI market

The AI market was estimated at $87.04 billion in 2021.

With a compound annual growth gate (CAGR) of 38.1% over the analysis period from 2022 to 2003, the market is projected to reach $1.59 trillion by the end of it.

Google is a leader in the North American AI market, with a market share of 42%. 

Vision and expectations

Through its AI initiatives, Google aspires to solve important issues from the corporate atmosphere to improving people’s daily lives. One of the main goals is to aid companies in better managing their data, making the jobs of data scientists and engineers easier and more efficient.

The engineers behind Vertex AI believe that making AI more universally accessible will promote innovation, enabling the creation of a future they can be optimistic about.

“We had two guiding lights while building Vertex AI: get data scientists and engineers out of the orchestration weeds and create an industry-wide shift that would make everyone get serious about moving AI out of pilot purgatory and into full-scale production,” says Andrew Moore, VP and GM of cloud AI and industry solutions at Google Cloud, at the 2021 Google I/O conference.

“We are very proud of what we came up with in this platform [Vertex AI], as it enables serious deployments for a new generation of AI that will empower data scientists and engineers to do fulfilling and creative work.”

Google Cloud Vertex AI features

Vertex AI hosts a Feature Store for ML capabilities, ranging from organizing and storing to serving and deployment with efficiency.

Google Cloud Vertex AI features include:

AutoML

AutoML is Google’s solution for automating ML processes in applications and problem solving

  • Develop high-quality custom ML models
  • Eliminates writing training routines for ML models
  • Powered by Google’s transfer learning and hyperparameter search tech

Deep learning VM images

Virtual machine (VM) images contain bootable virtual operating systems (OSs).

  • VM images with Google’s AI frameworks
  • Doesn’t require software compatibility

Vertex AI Workbench

Vertex AI Workbench is a unified environment for Google’s ML offerings.

  • ML experimentation, deployment, monitoring, and management
  • Jupyter-based and fully managed
  • Scalable and enterprise-ready

Vertex AI Matching Engine

The Matching Engine performs similarity matching based on vectors.

  • Highly scalable
  • Low latency
  • Cost-effective

Vertex AI data labeling

Data labeling is the processing of adding concise information labels to raw data using AI.

  • Human labelers for highly accurate labeling
  • Suitable results for ML models

Vertex AI deep learning containers

AI-powered deep learning containers are pre-installed images for fast ML model deployment.

  • Fast building and deployment of portable ML models
  • Offers a consistent environment for AI apps

Vertex AI edge manager

Edge Manager agent is the interfacing for managing edge devices through AI.

  • Seamless deployment and monitoring
  • Automated processing with flexible APIs

Vertex explainable AI

Explainable AI allows you to understand your model’s outputs, used in regression and classification tasks.

  • Actionable explanations of model predictions
  • Integrated into Vertex AI Prediction, AutoML Tables, and Vertex AI Workbench

Vertex ML Metadata

Vertex ML Metadata is a tool for recording an ML model’s metadata for analysis, debugging, performance auditing, and queries.

  • Easy-to-use Python SDK
  • Built-in tracking of ML workflows

Vertex AI Model Monitoring

Vertex AI Model Monitoring supports drift detection and feature skew for numerical and categorical features in ML models.

  • Automated alerts
  • Monitors model performance incidents

Vertex AI Neural Architecture Search

The Neural Architecture Search enables ML experts to perform complex tasks with high accuracy, low latency, and minimal power requirements.

  • Optimizes existing ML model architectures
  • Automated using Google’s AI research

Google Cloud Vertex AI benefits

Google Cloud’s Vertex AI helps ML engineers build, deploy, and scale ML models faster, more efficiently, and with 80% fewer lines of code than before using AI.

Vertex AI’s benefits are summarized in three key points:

Minimal coding expertise required

Vertex AI offers pre-trained APIs, allowing you to build ML models in less time and using less code. The APIs cover computer vision, language, structured data, and conversation applications.

Custom and pre-trained tooling

Going beyond the basics, Vertex AI’s tools can be used to build advanced ML models. Additionally, the models can be trained using custom libraries codeless.

Simplifies ML model management

Vertex AI’s MLOps tools eliminate the complexity of standard ML model maintenance. This includes the use of Vertex AI Pipelines for streamlining ML pipelines, and the Vertex AI Feature Store for AI tech and ML features.

Integrations

One of Vertex AI’s top qualities is its integration and compatibility with Google Cloud and its services. It brings all Google Cloud services for designing, building, and deploying ML models under a unified user interface and API. The product’s AutoML also enable ML engineers to build and train ML models with little to no code required, in addition to custom APIs all stored in a central model repository.

Google Cloud Vertex AI use cases

Google Cloud’s Vertex AI has various applications and use cases, both technical and practical applications with global corporations and businesses.

Subaru

Subaru is a Japanese manufacturer of automobiles, vehicles, aircraft, and space-related equipment. 

Looking to achieve a goal of zero fatal traffic accidents by 2030, Subaru opened an AI development base. Subaru chose Google Cloud, because it had numerous managed services for AI, such as Vertex AI and related training.

“I chose Google Cloud from many platforms because, at the time of selection, it had multiple managed services such as Vertex AI, the managed notebooks option, and Vertex AI Training that were useful for AI development,” says Toshimi Okubo, senior engineer of AI R&D section, Subaru.

Since adopting Vertex AI, Subaru’s employees were able to experiment freely and move the bulk of their work to the cloud.

Digits Financial

San Francisco-based Digits Financial is a fintech company that builds custom tools for businesses and finance professionals to help automate error detection in ledgers through smart suggestions. 

Digits was looking for an efficient and affordable way to speed up its time to market while maintaining model integrity. Using Vertex Pipelines, it was able to optimize governance, reusability, and orchestration in operations.

“Vertex Pipelines let us move faster from ML prototypes to production models and give us confidence that our ML infrastructure will keep pace with our transaction volume as we scale,” says Hannes Hapke, ML engineer, Digits Financial.

Using Vertex Pipelines, Digits increased the velocity of publishing and was able to meet the increased demand in the market.

Technical use cases

Google Cloud Vertex AI can be used in a wide variety of ways when it comes to ML models and data analysis:

Data readiness

Vertex AI allows you to ingest data from cloud storage and BigQuery and label it to improve prediction and analysis accuracy.

Feature engineering

Vertex AI Feature Store can be used for a variety of applications on ML models. This includes tracking, analyzing, and discovering ML experiments as well as using TensorBoard to visualize various ML experiments.

Training and hyperparameter tuning

You can use Vertex AI to build ML models without code and optimize their architecture for specific tasks. The training feature also enables you to use hyperparameters to optimize the predictive accuracy of models.

Model serving

Vertex AI simplifies the process of model deployment through HTTP and batch prediction. 

Edge

Vertex AI Edge Manager allows for the seamless deployment and monitoring of edge interfaces using flexible APIs. You can use it to distribute AI and ML models across the private and public cloud and on-premises infrastructure.

User reviews of Google Vertex AI

Google Vertex AI has an overall rating of 4.4 out of 5 at the user review site Gartner Peer Insights.

Conclusions

Vertex AI is a well-rounded platform dedicated to helping engineers create better ML models and simplify the deployment, management, and monitoring of completed models. Being under the Google Cloud umbrella, Vertex AI gains access to Google’s various AI-powered tools and features. Well-tested by numerous companies and highly rated by users, Vertex AI is a strong option for ML models.

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