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

Ekta S.

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

Validated Review

Verified Reviewer

Submitted Mar 2024

Google Vertex AI: Simplifying AI Development.

Likeliness to Recommend

9 /10

What differentiates Google Cloud Vertex AI from other similar products?

Google Cloud Vertex AI distinguishes itself by providing a comprehensive platform for machine learning development. Its unified approach streamlines the entire process from data preparation to model deployment. With powerful AutoML capabilities, it enables users to create custom models with ease. Integration with Google Cloud services enhances workflow efficiency. Moreover, Vertex AI offers scalability, performance, pre-built models, and advanced monitoring tools, making it a robust solution for businesses and developers in the AI space.

What is your favorite aspect of this product?

One of the standout features of Google Vertex AI is its seamless integration with Google Cloud services, which greatly simplifies the machine learning development process. Leveraging the extensive capabilities of Google Cloud allows for efficient data management, processing, and analysis, enabling developers to focus more on building and refining their models rather than managing infrastructure. This integration enhances productivity and accelerates the deployment of AI solutions, making Vertex AI a preferred choice for ML projects.

What do you dislike most about this product?

One potential drawback of Google Vertex AI could be its learning curve for users who are new to the platform or have limited experience with machine learning. While it offers powerful features and capabilities, navigating the interface and understanding the various components may require some time and effort. Additionally, the pricing structure of Google Cloud services, including Vertex AI, could be complex for some users to grasp initially, potentially leading to unexpected costs.

What recommendations would you give to someone considering this product?

Familiarize yourself with Google Cloud platform basics for seamless integration. Start with tutorials and documentation to understand Vertex AI's capabilities and workflow. Experiment with pre-built models and AutoML features to accelerate model development. Utilize Google Cloud's extensive resources for data management and analysis. Stay updated on new features and best practices through Google's support channels and community forums. Consider cost management strategies to optimize spending on resources and services.

Pros

Helps Innovate
Continually Improving Product
Performance Enhancing
Enables Productivity

Neelam V.

Role: Consultant
Industry: Engineering
Involvement: Business Leader or Manager

Validated Review

Verified Reviewer

Submitted Mar 2024

Robust functionality, yet lacks userfriendly .

Likeliness to Recommend

10 /10

What differentiates Google Cloud Vertex AI from other similar products?

Google Cloud Vertex AI sets itself apart with its integrated AI platform that simplifies the ML workflow, offers both AutoML and custom model support, provides pre-trained APIs for common tasks, includes advanced MLOps features for seamless model management, benefits from seamless integration with Google Cloud services, and leverages Google's infrastructure for scalability and performance.

What is your favorite aspect of this product?

My favorite aspect of Google Cloud Vertex AI is its integrated AI platform, which streamlines the entire machine learning workflow. This integration not only simplifies the process of developing, deploying, and managing ML models but also significantly reduces the complexity and time required to bring AI solutions to market.

What recommendations would you give to someone considering this product?

Start with the Documentation: Spend some time with Google Cloud's comprehensive documentation and tutorials. They provide a solid foundation for understanding how to use Vertex AI effectively, covering everything from basic concepts to advanced techniques. Experiment with AutoML: If you're new to machine learning or looking for a quick way to deploy models, begin with Vertex AI's AutoML capabilities. It's a great way to familiarize yourself with the platform's functionalities without deep technical knowledge in ML model development.

Pros

Helps Innovate
Continually Improving Product
Performance Enhancing
Enables Productivity

Shivam K.

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

Validated Review

Verified Reviewer

Submitted Mar 2024

Easy to use, but a bit pricey.

Likeliness to Recommend

9 /10

What differentiates Google Cloud Vertex AI from other similar products?

Google Cloud Vertex AI provides a unified AI platform for building, deploying, and managing machine learning models, offering autoML capabilities, and supporting TensorFlow, PyTorch, and XGBoost.

What is your favorite aspect of this product?

Google Cloud Vertex AI is its ability to simplify and streamline the machine learning workflow, making it easier for developers to build and deploy models.

What do you dislike most about this product?

Pricing of Google Cloud Vertex AI to be a bit complex or expensive, depending on their usage needs.

What recommendations would you give to someone considering this product?

1. Start with the free tier or use the pricing calculator to estimate costs.

Pros

Helps Innovate
Reliable
Performance Enhancing
Enables Productivity

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

Ekta S.

Google Vertex AI: Simplifying AI Development.

Likeliness to Recommend

Pros

Neelam V.

Robust functionality, yet lacks userfriendly .

Likeliness to Recommend

Pros

Shivam K.

Easy to use, but a bit pricey.

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