Engineering Services

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

What do we implement?

We develop, test, and operate engineering solutions along the complete lifecycle of data and machine learning projects. The unique combination of solid software engineering skills and extensive data and ML expertise enables us to deliver high-quality systems. In addition to developing data pipelines and ML models, we engineer software and services that either help jump-start your data and AI journey or lift you to the next level of deploying and operating your end-to-end systems. These include the implementation of architectures, analysis tools, data, machine learning, and code pipelines, as well as enterprise and production-grade systems.

01

Architecture

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

02

Data Engineering

We engineer custom-tailored data platforms and pipelines for your data management and product needs.

03

ML Engineering

We help you find the best use-case approach, develop reusable experiment pipelines, and test and deploy your ML system.

04

DataOps & MLOps

We design and implement our solutions so that they can be deployed with confidence and reliably run in production.

How we do it?

We develop systems with the ultimate aim of rolling them out as scalable and reliable products and running them in a production environment. To ensure seamless development and operation of our solutions, every line of code is written using the following DataOps, MLOps, and DevOps best practices and powered by Reliability Engineering.

  • 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.