We performed a comparison between Google Cloud Datalab and IBM Watson Studio based on real PeerSpot user reviews.
Find out in this report how the two Data Science Platforms solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."The APIs are valuable."
"The infrastructure is highly reliable and efficient, contributing to a positive experience."
"In MLOps, when we are designing the data pipeline, the designing of the data pipeline is easy in Google Cloud."
"Google Cloud Datalab is very customizable."
"All of the features of this product are quite good."
"It has a lot of data connectors, which is extremely helpful."
"It has greatly improved the performance because it is standardized across the company."
"The system's ability to take a look at data, segment it and then use that data very differently."
"It is a very stable and reliable solution."
"It stands out for its substantial AI capabilities, offering a broad spectrum of features for crafting solutions that meet specific requirements."
"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."
"Technical support is great. We have had weekly teleconferences with the technical people at IBM, and they have been fantastic."
"Stability-wise, it is a great tool."
"The interface should be more user-friendly."
"We have also encountered challenges during our transition period in terms of data control and segmentation. The management of each channel and data structure as it has its own unique characteristics requires very detailed and precise control. The allocation should be appropriate and the complexity increases due to the different time zones and geographic locations of our clients. The process usually involves migrating the existing database sets to gcp and ensure data integrity is maintained. This is the only challenge that we faced while navigating the integers of the solution and honestly it was an interesting and unique experience."
"The product must be made more user-friendly."
"Connectivity challenges for end-users, particularly when loading data, environments, and libraries, need to be addressed for an enhanced user experience."
"There is room for improvement in the graphical user interface. So that the initial user would use it properly, that would be a good option."
"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."
"So a better user interface could be very helpful"
"Watson Studio would be improved with a clearer path for the deployment of docker images."
"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."
"I think maybe the support is an area where it lacks."
"The solution's interface is very slow at times."
"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."
Google Cloud Datalab is ranked 16th in Data Science Platforms with 5 reviews while IBM Watson Studio is ranked 11th in Data Science Platforms with 13 reviews. Google Cloud Datalab is rated 7.6, while IBM Watson Studio is rated 8.2. The top reviewer of Google Cloud Datalab writes "Easy to setup, stable and easy to design data pipelines". 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 Cloud Datalab is most compared with Databricks, IBM SPSS Statistics, Cloudera Data Science Workbench, KNIME and Qlik Sense, whereas IBM Watson Studio is most compared with Databricks, Azure OpenAI, Microsoft Azure Machine Learning Studio, Google Vertex AI and Amazon Comprehend. See our Google Cloud Datalab vs. IBM Watson Studio report.
See our list of best Data Science Platforms vendors.
We monitor all Data Science 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.