Portable code has empowered cross‑client data science and now supports collaborative AI projectsMachine learning AI platforms like Domino Data Science Platform have the problem that they need to demonstrate well to citizen data scientists and technology management, so they need to make AI look easy. They also need to demonstrate well to professional data scientists who want to have their hands on as much power and scalability as possible. Lastly, they need to serve what we call MLOps engineers and software engineers to take the models and move them to production deployment. Those three personalities want a tool to look three different ways, and Domino Data Science Platform does a really good job at that. Domino Data Science Platform makes it accessible to citizen data scientists with basic concepts for IT management, has robust abilities to serve professional data scientists, and also has abilities to help MLOps and DataOps people deploy. I think the ability to select your environment, your containers, and your scalability, to use lots of computing memory when you need it, and then build your model, create your architectures, and then scale back to different sizes of the platforms as you need it is the area that Domino Data Science Platform stands out the most for professional data scientists. The ability to do the code yourself in Python and at the same time have automatic version control is really helpful too. I would highlight that Domino Data Science Platform is closer to Databricks, and while I have also used Dataiku, I would say that Dataiku is actually closer to the citizen data scientist platform.
