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Vertex AI

Score8.9 out of 10

26 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.

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.

Google's Vertex AI Is Great For Image Editing

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

Powerful complex with top of the line feature set

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

Vertex AI Review

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

Vertex AI in a glimse

Use Cases and Deployment Scope

I have been working on Vertex AI for quite long now, It is one stop all data science solution,I did transformed multiple POCs to projects with Vertex AI only. At the same time, it has lot many bugs google team tough fixes but still it has many bugs

Pros

  • Kubeflow pipeline
  • Model building

Cons

  • Kubeflow pipelines
  • Jupyter notebook, file upload environment issues

Return on Investment

  • Accelerated development
  • Focus on logic building and business
  • Faster gtm

Alternatives Considered

Azure AI Studio and Amazon Bedrock

Other Software Used

Looker Studio, Google BigQuery, Google Cloud Dialogflow

Automation with Vertex AI

Use Cases and Deployment Scope

we have used Vertex AI in machien learning models to streamline and accelerate the development. Vertex AI is help us too automate few things in our project as well and we are using google cloud already it's super easy to integrate with big query and dataflow it makes allowing smooth data pipeline and machine learning process

Alternatives Considered

Amazon SageMaker

Other Software Used

Amazon SageMaker, Azure Databricks