Google

Google Cloud Vertex AI

Composite Score

8.7 /10

CX Score

8.9 /10

81 Reviews Write a Review

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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.

Company Details

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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.

Product scores listed below represent current data. This may be different from data contained in reports and awards, which express data as of their publication date.

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

Product scores listed below represent current data. This may be different from data contained in reports and awards, which express data as of their publication date.

+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 81

Model Training

Data Exploration and Visualization

Data Labeling

Feature Engineering

Explainability

Data Ingestion

Openness and Flexibility

Pre-Packaged AI/ML Services

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

Luis A.

Role: Information Technology
Industry: Technology
Involvement: IT Leader or Manager

Validated Review

Verified Reviewer

Submitted Nov 2021

The challenges and solutions have become clearer

Likeliness to Recommend

9 /10

What differentiates Google Cloud Vertex AI from other similar products?

AutoML (VertexAI), a new GCP service, integrates with various open-source machine learning frameworks (TensorFlow, PyTorch, sci-kit-learn). As a consequence, we were able to shift from on-premises open source ML to the cloud while preserving the added value of AutoML. Vertex AI is a relatively new technology (in May 2021). I was thrilled to see that Google Cloud Machine Learning Engine has just recently introduced TensorFlow to the cloud, but rivals have already begun using AutoML, so this Google cloud service appears to be following and evolving.

What is your favorite aspect of this product?

It's a cloud-based service that's been there for a long time as TensorFlow, but it's lately been reformed as a new integrated ML platform, ContactCenterAI, VertexAI, DocumentAI, and the difficulties and solutions. AutoML, which is included in Vertex AI, is very important. AutoML is a method that has gained a lot of attention in the machine learning field in recent years, but it is incredibly welcome that it is now being covered in full in a cloud machine learning service that even novices can use with reasonable ease.

What do you dislike most about this product?

As said in the positives, improving issues and solutions is a good flow, and the AI and ML disciplines are rapidly increasing, therefore I'd want to see you extend the examples and templates while introducing new technologies.

What recommendations would you give to someone considering this product?

I was able to easily switch to the GCP cloud while receiving the added functionality of AutoML by utilizing PyTorch and SciKit-Learn in my local environment. It's wonderful that AutoML = Vertex AI is now available on GCP. We've been following Vertex AI since before it was launched, and it's now a reasonable quality as part of AutoML, and we're working hard to aggressively hire in-house people. The good news is that "it has evolved to the point where Vertex AI can be implemented" based on this approach.

Pros

Helps Innovate
Continually Improving Product
Reliable
Enables Productivity

Uğur Deniz Y.

Role: Information Technology
Industry: Technology
Involvement: Business Leader or Manager

Validated Review

Verified Reviewer

Submitted Nov 2021

Successful and user-friendly

Likeliness to Recommend

10 /10

What differentiates Google Cloud Vertex AI from other similar products?

The performance and recommendations of the application are very successful.

What is your favorite aspect of this product?

Fast, practical and fit for purpose. The results obtained are highly accurate.

What do you dislike most about this product?

It rarely slows down.

What recommendations would you give to someone considering this product?

It will be more efficient if you read the sources.

Pros

Continually Improving Product
Reliable
Performance Enhancing
Enables Productivity

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

Validated Review

Anonymous Reviewer

Submitted Oct 2020

A solid ML solution

Likeliness to Recommend

9 /10

Pros

Enables Productivity
Helps Innovate
Continually Improving Product
Reliable

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.

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

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

Explainability Ability to explain/ visualize the model.

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.

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

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

Luis A.

The challenges and solutions have become clearer

Likeliness to Recommend

Pros

Uğur Deniz Y.

Successful and user-friendly

Likeliness to Recommend

Pros

A solid ML solution

Likeliness to Recommend

Pros

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Feature Ratings

Model Training

Data Exploration and Visualization

Data Labeling

Feature Engineering

Explainability

Data Ingestion

Openness and Flexibility

Pre-Packaged AI/ML Services

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