We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
Domino provides a central system of record that keeps track of all data science activity across an organization. Domino helps data scientists seamlessly orchestrate AWS hardware and software toolkits, increase flexibility and innovation, and maintain required IT controls and standards. Organizations can automatically keep track of all data, tools, experiments, results, discussion, and models, as well as dramatically scale data science investments and impact decision-making across divisions. The platform helps organizations work faster, deploy results sooner, scale rapidly, and reduce regulatory and operational risk.
The DataScience.com Platform makes it easy and intuitive for data science teams to work collaboratively on the data-driven projects that transform how companies do business. Explore and visualize data, share analyses, deploy models into production, and track performance - all from one place.
Domino Data Science Platform is ranked 20th in Data Science Platforms with 1 review while Oracle DataScience.com Platform is ranked 30th in Data Science Platforms. Domino Data Science Platform is rated 7.0, while Oracle DataScience.com Platform is rated 0.0. The top reviewer of Domino Data Science Platform writes "Good scalability and stability but the predictive analysis feature needs improvement". On the other hand, Domino Data Science Platform is most compared with Databricks, Amazon SageMaker, Dataiku Data Science Studio, Alteryx and Microsoft Azure Machine Learning Studio, whereas Oracle DataScience.com Platform is most compared with Amazon SageMaker and Databricks.
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.