Google Vertex AI pros and cons

4.2 out of 5

13 reviews
100% willing to recommend

Pros & Cons summary

Google Vertex AI offers robust features such as multiple libraries reducing coding needs, effective monitoring, easy deployment through endpoints, and seamless AutoML integration, outperforming other platforms in deployment and configuration. However, it lacks image generators and text-to-speech solutions. Slow training times suggest potential for speed enhancement. While domain knowledge is vital for effectiveness, improving technical documentation and expanding the knowledge base could improve usability and user guidance for less technical users.

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Prominent pros & cons

PROS

Vertex AI eliminates the need for extensive coding with multiple libraries and low-code, no-code services.

The monitoring feature is highly valued by data scientists for tracking model performance.

Vertex AI seamlessly integrates with Google Cloud Platform for data storage and model deployment.

The platform offers powerful analytics and AutoML features that streamline machine-learning workflows.

Vertex AI is a comprehensive platform for managing, deploying, and monitoring machine-learning models, removing the need for multiple tools and platforms.

CONS

Google Vertex AI's customer support system is not yet stabilized, causing challenges for users needing assistance.

The platform lacks optimization, especially in terms of processing time, which can be a barrier for users handling large datasets.

Vertex AI's dependency on the developer's domain knowledge and technical expertise makes it difficult for those without a strong background in machine learning and programming.

Users have noted the need for enhanced technical documentation, as the existing resources are not comprehensive or easily accessible within the tool.

There is dissatisfaction with the performance of models developed using the AutoML feature in Vertex AI, indicating room for improvement in this area.

Google Vertex AI Pros review quotes

TuPhan

Data Engineer at a tech consulting company with 201-500 employees

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.