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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Real user data aggregated to summarize the product performance and customer experience.
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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
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Since last award
100 Plan to Renew
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Since last award
76 Satisfaction of Cost Relative to Value
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Since last award
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?
Pros
- Inspires Innovation
- Performance Enhancing
- Effective Service
- Respectful
How to read the Emotional Footprint
The Net Emotional Footprint measures high-level user sentiment towards particular product offerings. It aggregates emotional response ratings for various dimensions of the vendor-client relationship and product effectiveness, creating a powerful indicator of overall user feeling toward the vendor and product.
While purchasing decisions shouldn't be based on emotion, it's valuable to know what kind of emotional response the vendor you're considering elicits from their users.
Footprint
Negative
Neutral
Positive
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
Google Cloud Vertex AI Reviews
Ritika B.
- Role: Information Technology
- Industry: Technology
- Involvement: IT Development, Integration, and Administration
Submitted Aug 2024
Likeliness to Recommend
Pros
- Continually Improving Product
- Reliable
- Performance Enhancing
- Efficient Service
Siddharth C.
- Role: Information Technology
- Industry: Telecommunications
- Involvement: End User of Application
Submitted May 2024
Enhancing ML with vertex AI
Likeliness to Recommend
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
Urnisha S.
- Role: Student Academic
- Industry: Engineering
- Involvement: End User of Application
Submitted Apr 2024
"Powerful AI Tool" "Outstanding Features"
Likeliness to Recommend
What differentiates Google Cloud Vertex AI from other similar products?
Google Cloud Vertex AI stands out for its comprehensive portfolio of machine learning services, which provide seamless scalability and ease of use. Model creation, deployment, and maintenance are made easier by its unified platform, and it stands out from the competition thanks to cutting-edge capabilities like support for TensorFlow and AutoML that meet a variety of AI requirements.
What is your favorite aspect of this product?
One of my favorite features of Google Cloud Vertex AI is its effortless integration and scalability. It streamlines the whole AI workflow by offering a single platform for creating, deploying, and maintaining machine learning models. Its adaptability is further increased by the inclusion of sophisticated features like AutoML and TensorFlow support, which makes it a top option for companies wishing to effectively and easily leverage AI.
What do you dislike most about this product?
What I dislike the most about Google Cloud Vertex AI is its pricing structure, which I find complex and potentially costly.
What recommendations would you give to someone considering this product?
For those considering Google Cloud Vertex AI, I would recommend that they begin by defining their AI project requirements and budget constraints. They should use the platform's free tier and trial options to study its features and determine whether it fits their needs or not.
Pros
- Helps Innovate
- Continually Improving Product
- Reliable
- Enables Productivity
