Use of Vertex AI
I used Vertex AI for one of my client who needed to combine data science and ML engineering workflows. We also tried to build own ML tool …
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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.
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Imagen model for image generation
$0.0001
Cloud
Starting at
Text, chat, and code generation
$0.0001
Cloud
per 1,000 characters
Text data upload, training, deployment, prediction
$0.05
Cloud
per hour
Entry-level set up fee?
- Setup fee optional
For the latest information on pricing, visithttps://cloud.google.com/vertex…
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- Free/Freemium Version
- Premium Consulting/Integration Services
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What is Vertex AI?
Vertex AI is a fully-managed, unified AI development platform for building and using generative AI. It includes access to AI Studio, Agent Builder, and 130+ foundation models including Gemini 1.5 Pro.
Gemini, Google’s most capable multimodal models
Vertex AI offers access to Gemini models from Google. Gemini is capable of understanding virtually any input, combining different types of information, and generating almost any output. Prompt and test in Vertex AI with Gemini, using text, images, video, or code. Using Gemini’s advanced reasoning and state-of-the-art generation capabilities, developers can try sample prompts for extracting text from images, converting image text to JSON, and even generate answers about uploaded images to build next-gen AI applications.
In addition to Gemini, the service includes access to Gemma, a family of lightweight, open models built from the same research and technology used to create the Gemini models.
130+ generative AI models and tools
The service also offers access to models with first-party (Gemini, Imagen, Codey), third-party (Anthropic's Claude 3), and open models (Gemma, Llama 2) in Model Garden. Its extensions enable models to retrieve real-time information and trigger actions. Models can be customized to any use case with a variety of tuning options for Google's text, image, or code models. Generative AI models and fully managed tools help to prototype, customize, and integrate and deploy them into applications.
Open and integrated AI platform
Data scientists can move faster with Vertex AI Platform's tools for training, tuning, and deploying ML models.
Vertex AI notebooks, including Colab Enterprise or Workbench, are natively integrated with BigQuery providing a single surface across all data and AI workloads. Vertex AI Training and Prediction help reduce training time and deploy models to production with any open source frameworks and optimized AI infrastructure.
MLOps for predictive and generative AI
Vertex AI Platform provides purpose-built MLOps tools for data scientists and ML engineers to automate, standardize, and manage ML projects.
Modular tools help collaborate across teams and improve models throughout the entire development life cycle—identify the best model for a use case with Vertex AI Evaluation, orchestrate workflows with Vertex AI Pipelines, manage any model with Model Registry, serve, share, and reuse ML features with Feature Store, and monitor models for input skew and drift.
Agent Builder
Vertex AI Agent Builder enables developers to build and deploy enterprise ready generative AI experiences. It provides the convenience of a no code agent builder console alongside grounding, orchestration and customization capabilities. With Vertex AI Agent Builder developers can create a range of generative AI agents and applications grounded in their organization’s data.
Vertex AI Features
- Supported: Access to Gemini, a multimodal model from Google DeepMind
- Supported: Generative AI models and tools
- Supported: Open and integrated AI platform
- Supported: MLOps for predictive and generative AI
- Supported: Search and Conversation
Vertex AI Screenshots
Vertex AI Videos
What is Vertex AI?
End to End ML workflow with MLOps
Vertex AI Competitors
Vertex AI Technical Details
| Deployment Types | Software as a Service (SaaS), Cloud, or Web-Based |
|---|---|
| Operating Systems | Unspecified |
| Mobile Application | No |
Frequently Asked Questions
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.
Azure Machine Learning, Amazon SageMaker, and IBM Watson Studio on Cloud Pak for Data are common alternatives for Vertex AI.
Reviewers rate Configurability highest, with a score of 8.6.
The most common users of Vertex AI are from Mid-sized Companies (51-1,000 employees).
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(13)Attribute Ratings
Reviews
(1-5 of 5)Companies can't remove reviews or game the system. Here's why
April 29, 2024
Vertex AI - Disrupter in the AI space
We use Vertex AI on a daily basis. Vertex AI workbench forms the best kind of a platform for our daily AI/ML development. So the product that we are working on requires us to experiment our code on GPU systems and Vertex AI workbench gives us the resources that are required. Business problems addressed - Document processing platform and Scope is anything and everything under the bracket of document processing.
- One unified platform - Very helpful for managing the end to end data science lifecycles
- AutoML capabilities - We have several models which are trained via AutoML which streamlines the entire ML training process
- Model monitoring and management - Vertex AI provides some good tactics to deal with model monitoring which is a very important aspect of working with models today
- Customization of AutoML models - A must needed capability to be able to tweak hyperparameters and also working with different models
- Model Explainability -Providing more comprehensive explanations about how models are utilizing features could be very beneficial
- Model versioning and experiments tracking - Enhancing the versioning capability could be good for end users
- Vertex AI has potentially accelerated all development tasks by 70%. No hassle of setting up environments on local specially.
- It has led to reduced training time because scaling up is automatically taken care of by Vertex AI.
- It has led to a reduction in processing time of 40% by our document processing system product.
I have used AWS sagemaker is the past for AI/ML model development in my previous organization for everything. Sagemaker is good with respect to certain services but when we talk about Vertex AI in comparison, AutoML is the differentiator. AutoML is very strong and is able to delivery competent models. Sagemaker has autopilot which is not that strong in comparison.
April 25, 2024
An Effective Machine Learning Platform That Is Both Easy to Use and Has Robust Features
Score 9 out of 10
Vetted Review
Verified User
Vertex AI is a very user friendly machine learning platform which simplifies machine learning for beginners without much coding experience, plus new users can easily handle large datasets and complex models with it because of how simple and scalable it is. And since it is powered by google AI, it can easily integrate with the cloud and can solve diverse business problems while providing robust documentation and support.
- Supports large datasets and complex models.
- Scalable and can handle TBs of data.
- Simple, customizable interface.
- Safest data storage platform.
- Well documented and simple to use.
- Limited community support for Vertex AI as it relatively new product.
- Complex pricing structures and high cost.
- Needs better access management tools so that teams can collaborate easily.
- Safest data storage platform and is well-documented.
- Enhances team productivity and efficiency.
- Scalable but complex pricing structure.
April 18, 2024
Build and deploy your AI or ML model with Vertex AI.
Vertex AI is a very useful tool for us, it simplifies automation by consolidating functionalities for engineers and data scientists, aiding in model deployment, tweaking and monitoring. It also allows gpu allocation for cost management and is compatible with Google Cloud services, plus it has a user friendly interface and robust documentation. Lastly just like any other google service, it is both scalable and ideal for team collaboration which makes it a very valuable tool for AI development.
- It simplifies automation by consolidating functionalities at one place.
- Can allocate cpu, gpu and memory for cost management.
- Friendly interface and has very good documentation.
- Compatible with google cloud services and is scalable.
- Though we can manage our cost by manually allocating resources, it is still very costly and should reduce or change their pricing structure.
- Less customization so if you're an advanced user, you may face some limitation.
- It consolidated all the functionalities in one place for AI and ML development.
- Scalable and compatible with google cloud services.
- Though we can manually allocate resources, it is still very costly.
As an organization, we want to be at the forefront when it comes to adopting AI technologies. Be it to enhance operational efficiency of our own internal employees, or to streamlining customer experience, we know that we need to be AI-powered in order stay competitive.
That's where Vertex AI comes in. GCP's generous onboarding terms let us try out many solutions, build our PoCs, gauging adoption before committing to a solution.
- Vertex AI comes with support for LOTs of LLMs out of the box
- MLOps tools are available that help to standardize operational aspects
- Document AI is an out of the box feature that works just perfectly for our use cases of automating lots to tedious data extraction tasks from images as well as papers
- I do wish that Vertex AI has something like an index of technical terms, so people can learn to use it faster instead of seeing 1 new & strange term at a time
- As an organization, we have our extensive practices with DevOps, together with the familiar tools & pipelines, Vertex AI's having its own set of tools doesn't help with making the learning & integrating curves less steep
- Similar to lots of other AI offerings, Vertex AI docs tend to have lots of buzzwords yet few technical details, making it seems like reading ads instead of technical docs
- Generous onboarding terms make it very cost-effective to try out
- Ready-out-of-the-box solutions drastically reduce development efforts
- MLOps tools are built-in that help to reduce manual repetitive effort
Let's say that Azure OpenAI Service offers you exactly what you look for in simple-to-understand terms: your own private instance of OpenAI API backend.
It's quite hard to compare the two, as one is quite specific in what it offers, while the other is on the general side.
So basically, it comes down to whether you're just looking to try out to see what fit you or what you need vs looking for a specific solution that you already decided on.
March 19, 2024
User-Friendly AI Solution
Vertex AI is Google's Machine Learning Platform that helps to streamline diverse machine learning tasks and help build ML models. Our organization uses Vertex AI to measure project performance and predict customer behavior so as to make more accurate forecasts. It is extremely user-friendly and is accessible to users with different levels of training.
- It allows for customization of LLMs.
- It makes machine learning accessible to non-developers.
- One can create models without extensive coding.
- It does have lower capacities to digest data compared to other platforms.
- Many of the features are available only for paid users, which is costly. So it may not make sense for a smaller enterprise.
- The customer support for the platform can be improved.
- Since the platform can be used by non-developers, it has made the forecast modelling process in our company far more efficient.
- Since it is a cloud based platform, it has improved collaboration among colleagues.
- It has helped marketing to better target customers.
Vertex AI is much more accessible to non-developers than IBM's product. Moreover, Vertex AI integrates well with other Google products, enhancing its capabilities. A big plus is its integration with cloud storage, that allows for better management and access of data. In all honesty, it wasn't much of a difficult choice to choose Vertex AI.