We performed a comparison between Google Vertex AI and IBM Watson Studio based on real PeerSpot user reviews.
Find out in this report how the two AI Development Platforms solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."Google Vertex AI is an out-of-the-box and very easy-to-use solution."
"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."
"The monitoring feature is a true life-saver for data scientists. I give it a ten out of ten."
"Vertex AI possesses multiple libraries, so it eliminates the need for extensive coding."
"It provides the most valuable external analytics."
"The most important thing is that it's a multi-faceted solution. It's a kind of specialist, not a generalist. It can produce very specific information for the customer. It's totally different from Google or any search engine that produces generic information. It's specialty is that it's all on video."
"For me, the valuable feature of the solution is the one that I used, which was Jupyter notebooks."
"It has greatly improved the performance because it is standardized across the company."
"It is a very stable and reliable solution."
"Stability-wise, it is a great tool."
"The solution is very easy to use."
"The main benefit is the ease of use. We see a lot of engineers in our site and customers that really like the way the tools are able to work with the people."
"Watson Studio is very stable."
"It would be beneficial to have certain features included in the future, such as image generators and text-to-speech solutions."
"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."
"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."
"Google Vertex AI is good in machine learning and AI, but it lacks optimization."
"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."
"The main challenge lies in visibility and ease of use."
"It's sometimes easy to get lost given the number of images the solution opens up when you click on the mouse and the amount of different tabs."
"Initially, it was quite complex. For us, it was not only a matter of getting it installed, that was just a start. It was also trying to come up with a standard way of implementing it across the entire organization, which had been a challenge."
"We would like to see it more web-based with more functionality."
"I want IBM's technical support team to provide more specific answers to queries."
"I think maybe the support is an area where it lacks."
"The initial setup was complex."
"We would like to see it less as one big, massive product, but more based on smaller services that we can then roll out to consumers."
Google Vertex AI is ranked 3rd in AI Development Platforms with 5 reviews while IBM Watson Studio is ranked 7th in AI Development Platforms with 13 reviews. Google Vertex AI is rated 8.4, while IBM Watson Studio is rated 8.2. The top reviewer of Google Vertex AI writes "A user-friendly platform that automatizes machine learning techniques with minimal effort". On the other hand, the top reviewer of IBM Watson Studio writes "A highly robust and well-documented platform that simplifies the complex world of AI". Google Vertex AI is most compared with Azure OpenAI, Microsoft Azure Machine Learning Studio, Hugging Face, Amazon SageMaker and NVIDIA DGX Systems, whereas IBM Watson Studio is most compared with Databricks, Microsoft Azure Machine Learning Studio, Azure OpenAI, Amazon Comprehend and IBM SPSS Modeler. See our Google Vertex AI vs. IBM Watson Studio report.
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We monitor all AI Development Platforms reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.
