Vertex AI

Score8.6 out of 10

39 Reviews and Ratings

What is Vertex AI?

Vertex AI on Google Cloud is an MLOps solution, used to build, deploy, and scale machine learning (ML) models with fully managed ML tools for any use case.

Categories & Use Cases

Media

Screenshot of an introduction to generative AI on Vertex AI - Vertex AI Studio offers a Google Cloud console tool for rapidly prototyping and testing generative AI models.

Screenshot of gen AI for summarization, classification, and extraction - Text prompts can be created to handle any number of tasks with Vertex AI’s generative AI support. Some of the most common tasks are classification, summarization, and extraction. Vertex AI’s PaLM API for text can be used to design prompts with flexibility in terms of their structure and format.

Screenshot of Custom ML training overview and documentation - An overview of the custom training workflow in Vertex AI, the benefits of custom training, and the various training options that are available. This page also details every step involved in the ML training workflow from preparing data to predictions.

Screenshot of ML model training and creation - A guide that shows how Vertex AI’s AutoML is used to create and train custom machine learning models with minimal effort and machine learning expertise.

Screenshot of deployment for batch or online predictions - When using a model to solve a real-world problem, the Vertex AI prediction service can be used for batch and online predictions.

1 / 5

Screenshot of an introduction to generative AI on Vertex AI - Vertex AI Studio offers a Google Cloud console tool for rapidly prototyping and testing generative AI models.

Data management
Ingested data can be stored and prepped, with structures like data lakehouses available to handle large amounts of data from disparate sources.
Category average: 7.6
Automated model training
After teams begin the process, model training can continue autonomously, enabling faster deployment.
Category average: 7.6
Machine learning frameworks
A wide variety of machine learning frameworks are available and to be used when training models.
Category average: 7.6

Security and compliance
End to end encryption, GDPR compliance, SSO, role-based permissioning, and other precautions are available to protect proprietary business data.
Category average: 7.9
Data monitoring and version control
Teams can track which data is used in training at which point and roll back to previous versions as needed.
Category average: 7
Managed scaling
The platform provides the computing resources needed when they’re needed, allowing users to scale training and use up or down.
Category average: 7.3

Verified User

Analyst (10,001+ employees employees)

Use Cases and Deployment Scope

In our organisation we use Vertex AI to develop and deploy the ML models,its is very helpful and useful for our organisation,easy to automate the task and day to day activity,improving decision making and logical thinking,we using this AI different kind of scoop like data integration model training, monitoring and analysis part as well. we have analysed business level problem and strategic as well. reduced our manual works and implemented many automation's task and integrated with our internal product also.using this model agents will be deployed faster.improved our operation effect

Pros

automatic resource management
real time working experience
machine learning management

Cons

complex for beginners
step by step real world working
cost effective

Return on Investment

productive innovation and devolopment
cost effective
reduce infra cost for developing products
reduce training time

Alternatives Considered

ChatGPT and Anthropic Claude

Other Software Used

Vertex AI, Anthropic Claude, ChatGPT

Verified User

Administrator in Information Technology (10,001+ employees employees)

Use Cases and Deployment Scope

Vertex AI is the best AI, and I am currently using it for automation in Security Platforms, and it has the best accuracy to find vulnerabilities. The special in the Vertex AI is that we can train the model for our daily use case, and it is best in the output without false positives. We can use this to reduce the manpower and it will give the result within a few seconds according to our inputs. The best thing in the Vertex AI is pay as you go. It can be capable for the simple prompts and will give the results in a detailed manner. It will create the workflow for our daily tasks. Best AI with great accuracy.

Pros

To find the Vulnerabilities in the servers
To automate the daily checklist in our daily tasks Vertex AI will complete it within a minute
To create the workflow for the Security automations

Cons

It is little hard to experience with the Vertex AI
It has the low testing fields for the Security
In the SOC area need to be improved, as of now it has low in Defence.

Return on Investment

Need to automate the more workflow with the different Scenario
The cost of the Vertex AI is little high
It has the best to give the results within a seconds

Alternatives Considered

ChatGPT and Perplexity

Other Software Used

Vertex AI, ChatGPT, Perplexity

Jeff Montgomery

CTO in Manufacturing at Animal Necessity (11-50 employees employees)

Use Cases and Deployment Scope

We do a lot of image generation for our various areas of marketing (website, social media, graphics for conferences/booth giveaways), and we love using Vertex AI's image editor. We've used other AI image generators in the past (such as Microsoft's and Chat GPT's), but we've found Vertex to be a lot more accurate with less amount of mistakes.

Pros

Image Editing (especially with minute details)
Retained Image Crispness around edges of AI's additions
Amount of menu options Google provides in it's UI
Google's training modules on Vertex is actually really helpful

Cons

It's expensive to use. The cost could be a bit lower for tokens.
The Vertex AI Search isn't bulletproof and sometimes is wrong
To really master their UI, it does take some training. There's a lot of options.

Return on Investment

It has helped speed up the process of getting the exact image we need for our various marketing initiatives.
Images are stored in our Google Cloud, which we use in our business anyways (makes it super easy to find/share)
Google's security is always top notch. Even though it's just images, we still need the confidentiality of our creations.

Alternatives Considered

ChatGPT, Microsoft Copilot and OpenAI API Platform

Other Software Used

ChatGPT, Microsoft Copilot, OpenAI API Platform

Verified User

Technician in Information Technology (1001-5000 employees employees)

Use Cases and Deployment Scope

We work as experts of this platform and implement it in customers environments. It can range from banks to airlines so business use can vary a lot. Not one business is like the other so flexibility is a must for us, and we can quickly and easily see what platforms are adaptable and useful.

Pros

Integration to various platforms for multi-purpose
Flexibility of quick and easy adjustments
Bot reading from knowledge base

Cons

Learning curve
Tutorials
Labs to learn the tools

Return on Investment

The steep learning curve is definitely an investment needed to be made
The partnership program leaves a little bit more to be desired, this process should be made slightly easier
Product and platform itself is genuinely very powerful

Alternatives Considered

Azure AI Bot Service and ChatGPT

Other Software Used

ChatGPT, Azure AI Studio, Genesys Cloud CX, Webex Contact Center, Google Cloud Dialogflow

Verified User

Manager in Information Technology (10,001+ employees employees)

Pros

Python Notebook Scheduling
Pre-trained ML Models
Easy access to BigQuery Studio
In Model-Garden we can directly use GenAI models

Cons

Computing Power is very less for little more complex scripts
Notebook scheduling can be improved where we can schedule the notebooks except 2-3 days in each month.
It'll take so much time to start the instance

Return on Investment

It is pay as you go model so it'll save more cost of your org. In our case previously we used to incurred 1-2L/Month now we are reduced it to 80k-1L.
It'll help you save your model training & model selection time as it provides pre-trained models in autoML.
It'll help you in terms of Security wherein we can use row level security access to authorized persons.

Other Software Used

Microsoft Power BI, Google BigQuery, Microsoft Teams

IBM Watson Studio on Cloud Pak for Data

Amazon SageMaker

Azure Machine Learning

Vertex AI

What is Vertex AI?

Categories & Use Cases

Media

Key Features

Top Performing Features

Data management

Automated model training

Machine learning frameworks

Areas for Improvement

Security and compliance

Data monitoring and version control

Managed scaling

Reviews

Use Cases and Deployment Scope

Pros

Cons

Return on Investment

Alternatives Considered

Other Software Used

Use Cases and Deployment Scope

Pros

Cons

Return on Investment

Alternatives Considered

Other Software Used

Use Cases and Deployment Scope

Pros

Cons

Return on Investment

Alternatives Considered

Other Software Used

Use Cases and Deployment Scope

Pros

Cons

Return on Investment

Alternatives Considered

Other Software Used

Pros

Cons

Return on Investment

Other Software Used

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