Engineering Services

Trust our engineers to implement sustainable machine learning solutions for you.

What do we implement?

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.

01

Architecture

We help define and realize your IT infrastructure to become AI-ready.

02

Data Exploration

We engineer custom-tailored analytics tools to help you discover the value of your data assets.

03

Model Development

We develop reusable experiment pipelines to always find the best ML approach for your use case.

04

MLOps

We design our solutions so that they can be reliably run in production.

How we do it?

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:

  • Continuous Integration

  • Continuous Delivery

  • Continuous Monitoring

  • Continuous Training

Search interest in the four Cs as of 2022 according to Google Trends

Continuous Integration

With every code commit/push, we build, test, and package a complete ML pipeline.

Continuous Delivery

The outputs of the continuous integration step is deployed to the target environment. A new ML service is now running.

Continuous Monitoring

We monitor model and system performance with statistics about the live input data to determine whether retraining or a new model is required.

Continuous Training

During production, new data constantly arrives. This data should be used to retrain our model. This requires automated model and data validation.