Trust our engineers to implement sustainable machine learning solutions for you.
We develop, test, and operate engineering solutions along the complete lifecycle of a machine learning project. The unique combination of solid software engineering skills and extensive ML expertise enables us to deliver high-quality AI systems. In addition to developing ML models, we engineer software and services that either help jump-start your ML journey or lift you to the next level of deploying and operating your end-to-end systems. These include the implementation of AI architectures, data analysis tools, and full machine learning pipelines.
We help define and realize your IT infrastructure to become AI-ready.
We engineer custom-tailored analytics tools to help you discover the value of your data assets.
We develop reusable experiment pipelines to always find the best ML approach for your use case.
We design our solutions so that they can be reliably run in production.
We develop systems with the ultimate aim to be run in a production environment. To ensure seamless development and operations of our solutions, every line of code is written using the following MLOps best practices:
With every code commit/push, we build, test, and package a complete ML pipeline.
The outputs of the continuous integration step is deployed to the target environment. A new ML service is now running.
We monitor model and system performance with statistics about the live input data to determine whether retraining or a new model is required.
During production, new data constantly arrives. This data should be used to retrain our model. This requires automated model and data validation.