Partner, AI and Machine Learning at a consultancy with 11-50 employees
Real User
Top 10
Oct 5, 2026
I am a professional data scientist, and I use Domino Data Science Platform for basic data engineering and model development. I developed a model to help a client identify which customers they should cross-sell service to using Domino Data Science Platform.
Senior MLOPs Architect at a consultancy with 1,001-5,000 employees
Real User
Top 10
Oct 3, 2026
My main use case for Domino Data Science Platform varies due to the nature of this large bank, the leading bank in Europe, where we have numerous data science and machine learning models that need to go into production for many customer and internal employee applications for prediction, particularly in the insurance domain for predicting fraudulent cases, checking customer retention for insurance renewals, and providing financial recommendations. Therefore, the main use cases are primarily bank-related, and my role focuses on the deployment side of these models. Recently, I deployed a model for an email triaging use case using Domino Data Science Platform, which is specifically designed for machine learning model deployment, unlike other cloud providers that may not offer all services in one platform. For versioning code, I used GitLab, and for DevSecOps, I applied tools like SonarQube, Fortify, and Nexss to scan for vulnerabilities, enabling code scans. I then utilized Domino Data Science Platform for the continuous delivery part by leveraging its compute capabilities such as GPUs and CPUs to run automated jobs from Git for model deployment and experiment tracking used by data scientists. Once the best model is finalized, it gets deployed within Domino Data Science Platform's model registry, which is converted to endpoints for application consumption. We monitor drift with Domino Model Monitoring, and since Domino Data Science Platform lacks a built-in resource monitoring system, we utilize external tools such as Grafana and Prometheus to track CPU and other resource usage during this activity.
We used Domino Data Science Platform for developing and working with machine learning models. It facilitated end-to-end development processes. Domino is based on Git, enabling collaboration similar to using Git. Each user operates on their own equivalent of a branch or fork, and once finished, they can merge their changes with the main project.
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 am a professional data scientist, and I use Domino Data Science Platform for basic data engineering and model development. I developed a model to help a client identify which customers they should cross-sell service to using Domino Data Science Platform.
My main use case for Domino Data Science Platform varies due to the nature of this large bank, the leading bank in Europe, where we have numerous data science and machine learning models that need to go into production for many customer and internal employee applications for prediction, particularly in the insurance domain for predicting fraudulent cases, checking customer retention for insurance renewals, and providing financial recommendations. Therefore, the main use cases are primarily bank-related, and my role focuses on the deployment side of these models. Recently, I deployed a model for an email triaging use case using Domino Data Science Platform, which is specifically designed for machine learning model deployment, unlike other cloud providers that may not offer all services in one platform. For versioning code, I used GitLab, and for DevSecOps, I applied tools like SonarQube, Fortify, and Nexss to scan for vulnerabilities, enabling code scans. I then utilized Domino Data Science Platform for the continuous delivery part by leveraging its compute capabilities such as GPUs and CPUs to run automated jobs from Git for model deployment and experiment tracking used by data scientists. Once the best model is finalized, it gets deployed within Domino Data Science Platform's model registry, which is converted to endpoints for application consumption. We monitor drift with Domino Model Monitoring, and since Domino Data Science Platform lacks a built-in resource monitoring system, we utilize external tools such as Grafana and Prometheus to track CPU and other resource usage during this activity.
We used Domino Data Science Platform for developing and working with machine learning models. It facilitated end-to-end development processes. Domino is based on Git, enabling collaboration similar to using Git. Each user operates on their own equivalent of a branch or fork, and once finished, they can merge their changes with the main project.
We primarily use the solution for customer retention, but there are a lot of use cases for this particular product.