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
Luis A.
- Role: Information Technology
- Industry: Technology
- Involvement: IT Leader or Manager
Submitted Nov 2021
The challenges and solutions have become clearer
Likeliness to Recommend
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
Submitted Nov 2021
Successful and user-friendly
Likeliness to Recommend
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
Submitted Oct 2020
A solid ML solution
Likeliness to Recommend
Pros
- Enables Productivity
- Helps Innovate
- Continually Improving Product
- Reliable
