Partner, AI and Machine Learning at a consultancy with 11-50 employees
Real User
Top 10
Oct 5, 2026
The need for AI governance that includes measuring model drift and assessing issues of large language models is something that Domino Data Science Platform really needs to strengthen, but I don't think that is specific to a problem with Domino Data Science Platform; I think it is a general problem with machine learning platforms. Regarding Domino Data Science Platform's AI capabilities, I find its governance and security to be basic at present.
Senior MLOPs Architect at a consultancy with 1,001-5,000 employees
Real User
Top 10
Oct 3, 2026
Improvement areas for Domino Data Science Platform could relate to resource monitoring capabilities; adding visuals that stakeholders can review would enhance awareness of resource usage and its impact on applications and costs. Additionally, while we have Domino Model Monitoring, I would like to see more model monitoring options being developed to benefit us and data scientists. As an MLOps engineer, my primary concern focuses on continuous training and continuous integration and deployment, and I believe Domino Data Science Platform could enhance its services for monitoring capabilities. Specifically, improving drift reduction by analyzing ground truth data alongside model metrics would be beneficial, as would include automatic triggers for deployments within Domino Data Science Platform instead of relying on external tools such as Git.
The deployment of large language models (LLMs) could be improved. Currently, Domino provides a simple server that cannot handle big deployments, which is not suitable for LLMs.
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,...
The need for AI governance that includes measuring model drift and assessing issues of large language models is something that Domino Data Science Platform really needs to strengthen, but I don't think that is specific to a problem with Domino Data Science Platform; I think it is a general problem with machine learning platforms. Regarding Domino Data Science Platform's AI capabilities, I find its governance and security to be basic at present.
Improvement areas for Domino Data Science Platform could relate to resource monitoring capabilities; adding visuals that stakeholders can review would enhance awareness of resource usage and its impact on applications and costs. Additionally, while we have Domino Model Monitoring, I would like to see more model monitoring options being developed to benefit us and data scientists. As an MLOps engineer, my primary concern focuses on continuous training and continuous integration and deployment, and I believe Domino Data Science Platform could enhance its services for monitoring capabilities. Specifically, improving drift reduction by analyzing ground truth data alongside model metrics would be beneficial, as would include automatic triggers for deployments within Domino Data Science Platform instead of relying on external tools such as Git.
The deployment of large language models (LLMs) could be improved. Currently, Domino provides a simple server that cannot handle big deployments, which is not suitable for LLMs.
The predictive analysis feature needs improvement.