Google

Google Cloud Vertex AI

8.7 /10

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What is Google Cloud Vertex AI?

Cloud Machine Learning Engine is a managed service that lets developers and data scientists build and run superior machine learning models in production. Cloud ML Engine offers training and prediction services, which can be used together or individually. It has been used by enterprises to solve problems ranging from identifying clouds in satellite images, ensuring food safety, and responding four times faster to customer emails. The training and prediction services within ML Engine are now referred to as AI Platform Training and AI Platform Prediction.

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Awards & Recognition

Google Cloud Vertex AI won the following awards in the Machine Learning Platforms category

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2024 Data Quadrant Gold Medalist

Latest Award, 2024

1 Top Rated Capabilities

4 Top Rated Features

Latest Award, 2024

2023 Emotional Footprint Champion

2023

Strategy and Innovation
Service Experience
Contract Negotiation

2023

2023 Data Quadrant Gold Medalist

2023

5 Top Rated Capabilities

11 Top Rated Features

2023

3 Top Rated Capabilities

9 Top Rated Features

2022

Google Cloud Vertex AI Ratings

Real user data aggregated to summarize the product performance and customer experience.
Download the entire Product Scorecard to access more information on Google Cloud Vertex AI.

89 Likeliness to Recommend

1
Since last award

100 Plan to Renew

1
Since last award

76 Satisfaction of Cost Relative to Value

1
Since last award

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Emotional Footprint Overview

+92 Net Emotional Footprint

The emotional sentiment held by end users of the software based on their experience with the vendor. Responses are captured on an eight-point scale.

How much do users love Google Cloud Vertex AI?

3% Negative

3% Neutral

94% Positive

Pros

Inspires Innovation
Performance Enhancing
Effective Service
Respectful

Feature Ratings

Average 80

Model Training

Data Exploration and Visualization

Feature Engineering

Pre-Packaged AI/ML Services

Explainability

Data Labeling

Openness and Flexibility

Data Ingestion

Model Monitoring and Management

Algorithm Diversity

Data Pre-Processing

Vendor Capability Ratings

Average 78

Quality of Features

Ease of Data Integration

Ease of Implementation

Ease of Customization

Ease of IT Administration

Business Value Created

Breadth of Features

Vendor Support

Availability and Quality of Training

Product Strategy and Rate of Improvement

Usability and Intuitiveness

Google Cloud Vertex AI Reviews

Shane M.

Role: Human Resources
Industry: Technology
Involvement: End User of Application

Validated Review

Verified Reviewer

Submitted Aug 2024

To scale and deploy machine learning models.

Likeliness to Recommend

9 /10

What differentiates Google Cloud Vertex AI from other similar products?

Data scientists' and developers' workflows are made simpler by Vertex AI, which centralizes a variety of machine learning tools and services. Model development and deployment are accelerated by the use of AutoML and MLOps features, which lessen the need for human interaction.

What is your favorite aspect of this product?

For monitoring machine learning experiments and storing model versions, datasets, and training configurations, I enjoy using Google Cloud Vertex AI.

What do you dislike most about this product?

Although it provides AutoML tools, to fully utilize its advanced capabilities, one may need to have specialist understanding of both Google Cloud and machine learning.

What recommendations would you give to someone considering this product?

For training bespoke models with frameworks like TensorFlow, PyTorch, and scikit-learn by uploading your own data and models to train in the cloud, I suggest Google Cloud Vertex AI.

Pros

Helps Innovate
Continually Improving Product
Reliable
Performance Enhancing

Yan G.

Role: Sales Marketing
Industry: Education
Involvement: End User of Application

Validated Review

Verified Reviewer

Submitted Aug 2024

A perfect machine learning software.

Likeliness to Recommend

10 /10

What differentiates Google Cloud Vertex AI from other similar products?

Availability of pre built templates and intuitive software interface.

What is your favorite aspect of this product?

Availability of responsive technical support team and easy building up of artificial intelligence models.

What do you dislike most about this product?

The software is a great machine learning management platform. No aversion.

What recommendations would you give to someone considering this product?

I advocate doing enough research before making decision of implementing any software. This will ensure real business value and quality services.

Pros

Performance Enhancing
Enables Productivity
Unique Features
Effective Service

Ritika B.

Role: Information Technology
Industry: Technology
Involvement: IT Development, Integration, and Administration

Validated Review

Verified Reviewer

Submitted Aug 2024

Likeliness to Recommend

10 /10

Pros

Continually Improving Product
Reliable
Performance Enhancing
Efficient Service

Google Cloud Vertex AI

What is Google Cloud Vertex AI?

Company Details

Need Assistance?

Awards & Recognition

2024 Data Quadrant Gold Medalist

2023 Emotional Footprint Champion

2023 Data Quadrant Gold Medalist

Google Cloud Vertex AI Ratings

89 Likeliness to Recommend

1 Since last award

100 Plan to Renew

1 Since last award

76 Satisfaction of Cost Relative to Value

1 Since last award

Emotional Footprint Overview

+92 Net Emotional Footprint

How much do users love Google Cloud Vertex AI?

Pros

Feature Ratings Average rating of all product features, some of which may not be shown below

Model Training Ability to train a model with a range of data sets that you provide.

Data Exploration and Visualization Ability to visually manipulate, interact with, and explore data, and perform basic descriptive statistics and pattern detection.

Feature Engineering Ability to extract and build features from raw data.

Pre-Packaged AI/ML Services Pre-trained models and associated resources, e.g., for forecasting, recommendations, image and video analysis, NLP, voice, etc.

Explainability Ability to explain/ visualize the model.

Data Labeling Ability to annotate/ label data to create a training data set.

Openness and Flexibility Integration with open-source and 3P resources (languages, frameworks, libraries, etc.)

Data Ingestion Ability to access data from various sources on-prem and in the cloud and of various type: structured, unstructured, and semi-structured, real-time, etc.

Model Monitoring and Management Ability to monitor model use and performance in production, incl. identifying models for refresh and/or replacement.

Algorithm Diversity Provides multiple diverse ready-to-use machine learning algorithms out-of-the-box.

Data Pre-Processing Ability to wrangle or pre-process data to ready it for model training.

Vendor Capability Ratings Average rating of all vendor capabilities, some of which may not be shown below

Quality of Features Feature quality is just as important as quantity. Use this data to determine if this product will do what you're purchasing it to do, easily, intuitively, reliably, and effectively.

Ease of Data Integration Use this data to determine whether the product will cause headaches or make data integration easy.

Ease of Implementation Successfully implementing new software is necessary to realize its full value and promote end user adoption. This data indicates whether or not the product is easy to implement.

Ease of Customization Don't get bogged down in a difficult customization; use this data to make sure you can easily achieve the functionality you need for your particular situation.

Ease of IT Administration This data indicates whether IT personnel will be able to resolve issues and perform configurations efficiently and effectively.

Business Value Created Software needs to create value for employees, customers, partners, and, ultimately, shareholders. This data expresses user satisfaction - or lack thereof - with the product's business value.

Breadth of Features Users prefer feature rich software that enables them to perform diverse series of tasks. This data expresses user satisfaction with the product's breadth of features.

Vendor Support The importance of vendor support will vary for each organization depending on internal capabilities, but there will always be issues that only the vendor can resolve.

Availability and Quality of Training Effective and readily available training enables users to get the most out of the software you've chosen. Use this section to make sure your vendor's training programs and materials measure up.

Product Strategy and Rate of Improvement Vendors who don't stay on top of emerging needs and trends won't enable you to meet your business goals. Use this data to separate innovators from imposters.

Usability and Intuitiveness End user learning curves cost the organization money. Pay attention to your end users' technical ability to determine how important UX is in your purchase.

Google Cloud Vertex AI Reviews

Shane M.

To scale and deploy machine learning models.

Likeliness to Recommend

Pros

Yan G.

A perfect machine learning software.

Likeliness to Recommend

Pros

Ritika B.

Likeliness to Recommend

Pros

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Feature Ratings

Model Training

Data Exploration and Visualization

Feature Engineering

Pre-Packaged AI/ML Services

Explainability

Data Labeling

Openness and Flexibility

Data Ingestion

Model Monitoring and Management

Algorithm Diversity

Data Pre-Processing

Vendor Capability Ratings

Quality of Features

Ease of Data Integration

Ease of Implementation

Ease of Customization

Ease of IT Administration

Business Value Created

Breadth of Features

Vendor Support

Availability and Quality of Training

Product Strategy and Rate of Improvement

Usability and Intuitiveness