Partner, AI and Machine Learning at a consultancy with 11-50 employees
Real User
Top 10
Oct 5, 2026
I would rate Domino Data Science Platform an eight on a scale of one to ten. To be a ten, you would need to make a technology stack that is from the databases all the way through to advanced AI abilities and high security, so I don't think anyone could possibly be a ten. Domino Data Science Platform is an eight moving on a nine, and that is an impressive place to be. There is no other vendor that I would put at above an eight or a nine. Regarding Domino Data Science Platform's AI capabilities, I don't think the quality of the output is the platform's fault; the platform is used to support developers, so the quality of the outcome is really on the data scientist rather than the platform. My advice to others looking into using Domino Data Science Platform is that it is not lightweight; if you're looking for high ease of use for citizen data scientists and business intelligence tools, Domino Data Science Platform is not for you. Domino Data Science Platform is for full professional data scientists and professional data engineers who are collaborating on large teams, so it is an enterprise-class, high-power environment. I rate Domino Data Science Platform an eight out of ten.
Senior MLOPs Architect at a consultancy with 1,001-5,000 employees
Real User
Top 10
Oct 3, 2026
I rate Domino Data Science Platform a 10 out of 10 because of the numerous positives I have mentioned and the fact that I cannot think of any replacement other than Domino Data Science Platform. Domino Data Science Platform is continually evolving, and I look forward to the upcoming version that will feature enhancements such as AI and AI agents, which I believe will further improve our experience. Regarding the AI capabilities of Domino Data Science Platform, we had a workshop discussing AI governance and security, although I cannot comment extensively on it since we do not have those AI features and agents currently available in our bank. Domino Data Science Platform is deployed on-premises within our organization, specifically on IBM DNZR data systems, as we prioritize security and therefore do not utilize public cloud services. My advice for others looking to use Domino Data Science Platform is to leverage its full potential by understanding each layer of the deployment process, from model creation to project design, and ensuring clarity on where the code operates, its versioning, the environment structure, hardware resources available, and the applicable security levels for every service. I rate Domino Data Science Platform a 10 out of 10.
It's important to have a DevOps team well-versed with cloud-native solutions to manage Domino effectively. Relying solely on data scientists might not be sufficient. I'd rate the solution eight out of ten.
Find out what your peers are saying about Domino Data Lab, Amazon Web Services (AWS), Databricks and others in Data Science Platforms. Updated: October 2026.
Data Science Platforms facilitate data analysis, machine learning, and predictive modeling. These platforms integrate tools and libraries, streamlining the workflow for data scientists.Data Science Platforms support a wide range of data analytics and machine learning tasks, making them essential for data-driven decision-making. They offer robust tools for data preparation, model building, and deployment. By integrating with various data sources and supporting multiple programming languages,...
I would rate Domino Data Science Platform an eight on a scale of one to ten. To be a ten, you would need to make a technology stack that is from the databases all the way through to advanced AI abilities and high security, so I don't think anyone could possibly be a ten. Domino Data Science Platform is an eight moving on a nine, and that is an impressive place to be. There is no other vendor that I would put at above an eight or a nine. Regarding Domino Data Science Platform's AI capabilities, I don't think the quality of the output is the platform's fault; the platform is used to support developers, so the quality of the outcome is really on the data scientist rather than the platform. My advice to others looking into using Domino Data Science Platform is that it is not lightweight; if you're looking for high ease of use for citizen data scientists and business intelligence tools, Domino Data Science Platform is not for you. Domino Data Science Platform is for full professional data scientists and professional data engineers who are collaborating on large teams, so it is an enterprise-class, high-power environment. I rate Domino Data Science Platform an eight out of ten.
I rate Domino Data Science Platform a 10 out of 10 because of the numerous positives I have mentioned and the fact that I cannot think of any replacement other than Domino Data Science Platform. Domino Data Science Platform is continually evolving, and I look forward to the upcoming version that will feature enhancements such as AI and AI agents, which I believe will further improve our experience. Regarding the AI capabilities of Domino Data Science Platform, we had a workshop discussing AI governance and security, although I cannot comment extensively on it since we do not have those AI features and agents currently available in our bank. Domino Data Science Platform is deployed on-premises within our organization, specifically on IBM DNZR data systems, as we prioritize security and therefore do not utilize public cloud services. My advice for others looking to use Domino Data Science Platform is to leverage its full potential by understanding each layer of the deployment process, from model creation to project design, and ensuring clarity on where the code operates, its versioning, the environment structure, hardware resources available, and the applicable security levels for every service. I rate Domino Data Science Platform a 10 out of 10.
It's important to have a DevOps team well-versed with cloud-native solutions to manage Domino effectively. Relying solely on data scientists might not be sufficient. I'd rate the solution eight out of ten.
We use the on-premises deployment model. I'd rate the solution seven out of ten.