Google Vertex AI vs IBM Watson Studio comparison

"The monitoring feature is a true life-saver for data scientists. I give it a ten out of ten."

"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."

"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."

"Google Vertex AI is an out-of-the-box and very easy-to-use solution."

"It provides the most valuable external analytics."

"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."

"The integration of AutoML features streamlines our machine-learning workflows."

"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."

"The solution is very easy to use."

"The scalability of IBM Watson Studio is great."

"IBM Watson Studio consistently automates across channels."

"The system's ability to take a look at data, segment it and then use that data very differently."

"It is a stable, reliable product."

"It stands out for its substantial AI capabilities, offering a broad spectrum of features for crafting solutions that meet specific requirements."

"The tool's documentation is not good. It is hard."

"I think the technical documentation is not readily available in the tool."

"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."

"It would be beneficial to have certain features included in the future, such as image generators and text-to-speech solutions."

"I'm not sure if I have suggestions for improvement."

"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."

"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 think maybe the support is an area where it lacks."

"I want IBM's technical support team to provide more specific answers to queries."

"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."

"So a better user interface could be very helpful"

"The initial setup was complex."

"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."

"More features in data virtualization would be helpful. The solution could use an interactive dashboard that could make exploration easier."

"We would like to see it more web-based with more functionality."

"The price structure is very clear"

"The Versa AI offers attractive pricing. With this pricing structure, I can leverage various opportunities to bring value to my business. It's a positive aspect worth considering."

"I think almost every tool offers a decent discount. In terms of credits or other stuff, every cloud provider provides a good number of incentives to onboard new clients."

"The solution's pricing is moderate."

"IBM Watson Studio is a reasonably priced product"

"The pricing is generally reasonable and straightforward but can vary significantly depending on the specific workloads in use."

"Watson Studio's pricing is reasonable for what you get."

"IBM Watson Studio is an expensive solution."