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Google Vertex AI pros and cons

Vendor: Google
4.2 out of 5
Badge Ranked 1
128 followers
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Pros & Cons summary

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Prominent pros & cons

PROS

Vertex AI eliminates the need for extensive coding with its multiple libraries.
The monitoring feature provides data scientists with essential insights into model performance.
Vertex AI offers integration with AutoML, streamlining machine-learning workflows.
The model garden feature enables easy deployment and use of various models through provided endpoints.
Vertex AI's integration with GCP includes data storage options.

CONS

It would be beneficial to include features such as image generators and text-to-speech solutions in the future.
Vertex AI's training time could be improved as it is currently quite slow.
Enhancing the knowledge base and expanding information sources is essential for Vertex AI.
Vertex AI lacks optimization and its effectiveness heavily relies on the domain knowledge of the developer.
Documentation for Vertex AI is insufficient and challenging to navigate.
 

Google Vertex AI Pros review quotes

TuPhan - PeerSpot reviewer
Nov 28, 2023
We extensively utilize Google Cloud's Vertex AI platform for our machine learning workflows. Specifically, we leverage the IO branch for EDA data in Suresh Live Virtual, employing Forte IT for training machine learning models. The AI model registry in Vertex AI is crucial for cataloging and managing various versions of the models we develop. When it comes to deploying models, we rely on Google Cloud's AI Prediction service, seamlessly integrating it into our workflow for real-time predictions or streaming. For monitoring and tracking the outcomes of model development, we employ Vertex AI Monitoring, ensuring a comprehensive understanding of the model's performance and results. This integrated approach within Vertex AI provides a unified platform for managing, deploying, and monitoring machine learning models efficiently.
reviewer1285629 - PeerSpot reviewer
Aug 1, 2024
The most valuable features of the solution are that it is quite flexible, and some of the services are almost low-code, with no-code services, so it gives agents flexibility to build the use cases according to the operational needs.
Ivy W. - PeerSpot reviewer
Aug 31, 2023
The monitoring feature is a true life-saver for data scientists. I give it a ten out of ten.
Learn what your peers think about Google Vertex AI. Get advice and tips from experienced pros sharing their opinions. Updated: December 2024.
824,067 professionals have used our research since 2012.
Serge Dahdouh - PeerSpot reviewer
Aug 16, 2023
Vertex AI possesses multiple libraries, so it eliminates the need for extensive coding.
Srikar Kumar - PeerSpot reviewer
Nov 9, 2023
It provides the most valuable external analytics.
Kashif Yaseen - PeerSpot reviewer
Nov 11, 2024
Vertex comes with inbuilt integration with GCP for data storage.
Suresh Ayyavoo - PeerSpot reviewer
Nov 24, 2023
Google Vertex AI is an out-of-the-box and very easy-to-use solution.
Rajeev-Dave - PeerSpot reviewer
Sep 13, 2024
The integration of AutoML features streamlines our machine-learning workflows.
Kartik Singh - PeerSpot reviewer
Jul 19, 2024
The most valuable feature we've found is the model garden, which allows us to deploy and use various models through the provided endpoints easily.
 

Google Vertex AI Cons review quotes

TuPhan - PeerSpot reviewer
Nov 28, 2023
I believe that Vertex AI is a robust platform, but its effectiveness depends significantly on the domain knowledge of the developer using it. While Vertex AI does offer support through the console UI in the Google Cloud environment, it is better suited for technical members who have a deeper understanding of machine learning concepts. The platform may be challenging for business process developers (BPDUs) who lack extensive technical knowledge, as it involves intricate customization and handling numerous parameters. Effectively utilizing Vertex AI requires not only familiarity with machine learning frameworks like TensorFlow or PyTorch but also a proficiency in Python programming. The complexity of these requirements might pose challenges for less technically oriented users, making it crucial to have a solid foundation in both machine learning principles and Python coding to extract the full value from Vertex AI. It would be beneficial to have a streamlined process where we can leverage the capabilities of Vertex AI directly through the BigQuery UI. This could involve functionalities such as creating machine learning models within the BigQuery UI, providing a more user-friendly and integrated experience. This would allow users to access and analyze data from BigQuery while simultaneously utilizing Vertex AI to build machine learning models, fostering a more cohesive and efficient workflow.
reviewer1285629 - PeerSpot reviewer
Aug 1, 2024
I think the technical documentation is not readily available in the tool.
Ivy W. - PeerSpot reviewer
Aug 31, 2023
The solution is stable, but it is quite slow. Maybe my data is too large, but I think that Google could improve Vertex AI's training time.
Learn what your peers think about Google Vertex AI. Get advice and tips from experienced pros sharing their opinions. Updated: December 2024.
824,067 professionals have used our research since 2012.
Serge Dahdouh - PeerSpot reviewer
Aug 16, 2023
It would be beneficial to have certain features included in the future, such as image generators and text-to-speech solutions.
Srikar Kumar - PeerSpot reviewer
Nov 9, 2023
I've noticed that using chat activity often presents a broader range of options and insights for a well-constructed question. Improving the knowledge base could be a key aspect for enhancement—expanding the information sources to enhance the generation process.
Kashif Yaseen - PeerSpot reviewer
Nov 11, 2024
I'm not sure if I have suggestions for improvement.
Suresh Ayyavoo - PeerSpot reviewer
Nov 24, 2023
Google Vertex AI is good in machine learning and AI, but it lacks optimization.
Rajeev-Dave - PeerSpot reviewer
Sep 13, 2024
Both major systems, Azure and Google, are not yet stabilized, especially their customer support.
Kartik Singh - PeerSpot reviewer
Jul 19, 2024
The tool's documentation is not good. It is hard.