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."
"Vertex AI possesses multiple libraries, so it eliminates the need for extensive coding."
"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."
"It provides the most valuable external analytics."
"The solution is very easy to use."
"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."
"It is a very stable and reliable solution."
"Technical support is great. We have had weekly teleconferences with the technical people at IBM, and they have been fantastic."
"The scalability of IBM Watson Studio is great."
"Watson Studio is very stable."
"It stands out for its substantial AI capabilities, offering a broad spectrum of features for crafting solutions that meet specific requirements."
"Stability-wise, it is a great tool."
"It would be beneficial to have certain features included in the future, such as image generators and text-to-speech solutions."
"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."
"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."
"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."
"Google Vertex AI is good in machine learning and AI, but it lacks optimization."
"So a better user interface could be very helpful"
"The main challenge lies in visibility and ease of use."
"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."
"Watson Studio would be improved with a clearer path for the deployment of docker images."
"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."
"The initial setup was complex."
"Some of the solutions are really good solutions but they can be a little too costly for many."
"More features in data virtualization would be helpful. The solution could use an interactive dashboard that could make exploration easier."
Google Vertex AI is ranked 3rd in AI Development Platforms with 5 reviews while IBM Watson Studio is ranked 8th 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, Azure OpenAI, Microsoft Azure Machine Learning Studio, Amazon Comprehend and Anaconda. 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.
