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."In MLOps, when we are designing the data pipeline, the designing of the data pipeline is easy in Google Cloud."
"The APIs are valuable."
"Google Cloud Datalab is very customizable."
"The infrastructure is highly reliable and efficient, contributing to a positive experience."
"All of the features of this product are quite good."
"It stands out for its substantial AI capabilities, offering a broad spectrum of features for crafting solutions that meet specific requirements."
"The system's ability to take a look at data, segment it and then use that data very differently."
"Stability-wise, it is a great tool."
"It is a very stable and reliable solution."
"The solution is very easy to use."
"It is a stable, reliable product."
"For me, the valuable feature of the solution is the one that I used, which was Jupyter notebooks."
"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."
"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 interface should be more user-friendly."
"Connectivity challenges for end-users, particularly when loading data, environments, and libraries, need to be addressed for an enhanced user experience."
"The product must be made more user-friendly."
"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 more web-based with more functionality."
"More features in data virtualization would be helpful. The solution could use an interactive dashboard that could make exploration easier."
"Watson Studio would be improved with a clearer path for the deployment of docker images."
"The main challenge lies in visibility and ease of use."
"The decision making in their decision making feature is less good than other options."
"So a better user interface could be very helpful"
"The initial setup was complex."
"The solution's interface is very slow at times."
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.
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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.