Improving data scientist productivity by 80%


with the implementation of an end-to-end machine learning platform

Photo by Scott Graham from Unsplash

“ Fluid has simplified out model building and deployment process, and allowed for integration across our various platforms ”

Rainer Schilder - Lead Data Scientist

Case Study



Company Size

51 - 200 employees


Length 7 months

Team Size

3.2 FTE

Problem Space

Data Science



Machine Learning

Technology Used

Python   Kubernetes

FastAPI   Kubeflow

KServe   AWS

ScikitLearn   Tensorflow

Business Context

At a fast-paced technology company, the product owners have a long list of backlogs driving high demand of the data scientists attention. The complex technology landscape and disparate data systems create a high friction environment for the data scientists to efficiently creating insights and models. The average time from a problem to deployment takes 3+ months, resulting in the business losing competitive edge.


Use cases take a long time to go from conception to production

Data scientists lack the bandwidth to support all business use cases. This is compounded by the complex development & deployment environment, resulting in frustrated business & technical team.


Creating an MLOps platform to support use case development & deployment

The platform allows the users to upload a dataset, create analysis and data transformation, run AutoML using state-of-the-art machine learning algorithms and deploy the model into production.

End-to-end MLOps Platform

The fast-iterative feedback loop improves efficiency and improves ROI


20% cost reduction in operational cost in deployed machine learning


Time saved for data scientist in deploying models into production


Increased collaboration between business users & technical team