Data Science Platforms are Bad


A data science platform is an integrated set of tools that deliver the capabilities that most data science teams need. These capabilities are:

-The ability to do exploratory data analysis and create machine learning models.
-The ability to deploy models as APIs for other teams to use.
-The ability to schedule jobs and data pipelines to keep the business running.
-The ability to deploy dashboards for executives and stakeholders to view at any time.
-The ability to collaborate between members of the team easily on their work

There is nothing inherently wrong with the above. The problem is that most products attempt to provide these capabilities, by locking you in to specific workflows and paradigms. Data Science, since it’s focused on research, fundamentally has a lot of uncertainty. You never know what methods or libraries you’ll need to try next week. Agility and flexibility are critical. This talk focuses on how you can secure those capabilities for your team, without getting locked in.


Hugo Shi a data science leader with 15 years of experience with data science and software projects at companies ranging from JP Morgan to the Chicago Trading Company. He is the CTO and co-founder of Saturn Cloud where he helps to make sure that Saturn Cloud is secure, scalable and easy to use for all data science teams. Hugo has a PhD in Signal Processing and his academic research focused on iterative reconstruction algorithms in medical imaging.

Open Data Science




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Cambridge, MA 02142

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