Google

Google Cloud Vertex AI

Composite Score

8.7 /10

CX Score

8.9 /10

86 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 Emotional Footprint Champion

Latest Award, 2024

Service Experience

Latest Award, 2024

2024 Data Quadrant Gold Medalist

2024

1 Top Rated Capabilities

4 Top Rated Features

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

100 Plan to Renew

77 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

2% Neutral

95% Positive

Pros

Performance Enhancing
Inspires Innovation
Respectful
Reliable

Feature Ratings

Average 81

Model Training

Data Exploration and Visualization

Explainability

Feature Engineering

Data Labeling

Model Monitoring and Management

Data Ingestion

Openness and Flexibility

Pre-Packaged AI/ML Services

Model Tuning

Algorithm Diversity

Vendor Capability Ratings

Average 79

Ease of Customization

Quality of Features

Ease of Data Integration

Ease of Implementation

Ease of IT Administration

Business Value Created

Breadth of Features

Availability and Quality of Training

Vendor Support

Product Strategy and Rate of Improvement

Usability and Intuitiveness

Google Cloud Vertex AI Reviews

Siddharth C.

Role: Information Technology
Industry: Telecommunications
Involvement: End User of Application

Validated Review

Verified Reviewer

Submitted May 2024

Enhancing ML with vertex AI

Likeliness to Recommend

9 /10

What differentiates Google Cloud Vertex AI from other similar products?

Since vertex Ai is part of Google cloud environment the data we use for model development is easy to integrate and provides more layers of security. Integrating with data pipelines and other dependencies is made easier. Auto ML features are also more profound compared to other platforms.

What is your favorite aspect of this product?

Use of tensor flow for processing and other google owned libraries and resources makes it lot faster and convinient for model development and deployment. Deployment to cloud is also a matter of minutes.

What do you dislike most about this product?

Notebooks in vertex Ai could be mare more in sync with other googles visualization tools for the Analytical team with less coding base can visualize data that is in transit instead to wait till finish of the model.

What recommendations would you give to someone considering this product?

People who operate on cloud based environments should consider this as a choice without thinking much. It is cost efficient, user friendly, anyone who used Jupiter or Google Collab can easily be familiar with the environment.

Pros

Efficient Service
Reliable
Performance Enhancing
Enables Productivity

Cons

Vendor's Interest First
Under Delivered

Atharva V.

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

Validated Review

Verified Reviewer

Submitted Apr 2024

Exciting Potential of Vertex AI

Likeliness to Recommend

10 /10

What differentiates Google Cloud Vertex AI from other similar products?

Google Cloud Vertex AI is unique because it easily fits into Google Cloud's setup, making it simple to work with services such as BigQuery and TensorFlow. Plus, its automated machine learning tools make it easier for users at any skill level to build models, simplifying everything from preparing data to putting models into implementation.

What is your favorite aspect of this product?

What I really like about this product is how easily it works alongside other Google Cloud services, making everything flow more smoothly. Also, the automated machine learning tools are a big help in simplifying the process of building models, especially for someone like me.

What do you dislike most about this product?

My biggest issue with this product is its lack of support for some advanced customization choices. Also, I find the pricing a bit confusing and high.

What recommendations would you give to someone considering this product?

Use the trial to test its features and gather feedback from other users to make an decision.

Pros

Helps Innovate
Enables Productivity
Unique Features
Inspires Innovation

Cons

Less Generous

Bharti K.

Role: Finance
Industry: Finance
Involvement: Business Leader or Manager

Validated Review

Verified Reviewer

Submitted Mar 2024

Powerful ML platform with seamless integration

Likeliness to Recommend

8 /10

What differentiates Google Cloud Vertex AI from other similar products?

Google Cloud Vertex AI is distinguished by its deep integration with Google Cloud, providing a seamless experience for ML development and deployment. Its unified platform simplifies the entire ML lifecycle, offering both AutoML for quick model development and custom model training with TensorFlow for advanced users. This integration enables easy scalability and efficiency, with access to additional Google Cloud services for enhanced functionality.

What is your favorite aspect of this product?

My favorite aspect of Google Cloud Vertex AI is its seamless integration with other Google Cloud services. This integration makes it easy to leverage additional functionalities like data storage, processing, and analysis, all within the same platform. It streamlines the ML development process and allows for efficient scaling of ML workloads.

What do you dislike most about this product?

I dislike the complexity of setting up and managing ML workflows, especially for those new to machine learning. Additionally, the cost of using Google Cloud Vertex AI, especially for large-scale projects, could be a concern for some users.

What recommendations would you give to someone considering this product?

For those considering Google Cloud Vertex AI, I recommend starting with its AutoML features for easy model development. Leverage the integration with other Google Cloud services for enhanced functionality and cost-effectiveness. Be mindful of costs for large-scale projects and explore documentation for guidance. For advanced needs, utilize custom model training with TensorFlow. Scalability is a key advantage, ensuring models can grow with your business.

Pros

Reliable
Performance Enhancing
Enables Productivity
Effective Service

Cons

Less Friendly Negotiation
Vendor's Interest First

Google Cloud Vertex AI

What is Google Cloud Vertex AI?

Company Details

Need Assistance?

Awards & Recognition

2024 Emotional Footprint Champion

2024 Data Quadrant Gold Medalist

2023 Emotional Footprint Champion

2023 Data Quadrant Gold Medalist

Google Cloud Vertex AI Ratings

89 Likeliness to Recommend

100 Plan to Renew

77 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.

Explainability Ability to explain/ visualize the model.

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

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

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

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 Tuning 'Ability to fine-tune models based on monitoring feedback within the platform.

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

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

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.

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 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.

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.

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.

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

Siddharth C.

Enhancing ML with vertex AI

Likeliness to Recommend

Pros

Cons

Atharva V.

Exciting Potential of Vertex AI

Likeliness to Recommend

Pros

Cons

Bharti K.

Powerful ML platform with seamless integration

Likeliness to Recommend

Pros

Cons

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1
Since last award

Feature Ratings

Model Training

Data Exploration and Visualization

Explainability

Feature Engineering

Data Labeling

Model Monitoring and Management

Data Ingestion

Openness and Flexibility

Pre-Packaged AI/ML Services

Model Tuning

Algorithm Diversity

Vendor Capability Ratings

Ease of Customization

Quality of Features

Ease of Data Integration

Ease of Implementation

Ease of IT Administration

Business Value Created

Breadth of Features

Availability and Quality of Training

Vendor Support

Product Strategy and Rate of Improvement

Usability and Intuitiveness