Machine Learning Devops and Infrastructure

To help a customer facilitate rapid prototyping with Machine Learning algorithms we delivered a full stack machine-learning-pipeline optimized for multi stream time-series data. This enabled the customer to create and deploy Machine Learning algorithms within hours, while increasing predictability and reproducibility in their development workflow.

We also support clients with extending and refining their existing infrastructure to integrate Machine Learning workflows, as well as provide advice on data strategy, and generalist data science support. As an example in this area, we are currently working with Kollektiv, a startup providing a platform for personalized training for triathletes.

Production quality

Our client sought to improve the total quality of their production line. To achieve this they needed readily digestible insights into their KPIs. Working with the client we developed algorithms for deployment within their production and testing infrastructure. This enabled us to deliver an interactive dashboard where the client could easily view metrics for a given period, such as the nth -pass yield across each workstation, and most significant error codes.