Cloud Architecture & MLOps

Many business struggle to take data-projects from the proof-of-concept stage and fully operationalise and integrate it into business processes and the technology landscape. This maturity issue is deeply intertwined with make-or-buy decisions around cloud platforms, compute engines and open AI - in short: the right architecture. 

Depending on your needs, our experts can support you both in the design and implementation of a mature architecture for data products. Feel free to book a non-committal video call with our subject-matter expert to get an idea of the value, feasibility and scope. Typical projects are:

ML-Architecture

Our clients benefit from our experience in running, building and being part of many data science teams and their technical platforms. In the early stage of your data science department, you need to make strategic technical decisions on what your infrastructure should look like. Where to save your models and how to get your predictions in touch with the end user in a monitores, secure and scalable way. We have your long-term goals and challenges in mind while driving the early return of value from your data science team.

End-to-End ML Workflow Lifecycle

From the early stages of a machine learning model - possibly in Jupyter notebooks - until the deployment into production. This is not the end of the ML lifecycle. Monitoring data drift and concept shift, replacing models if needed or scaling their usage to new stakeholders - our MLOps experts support you on every step and make sure that your infrastructure is doing the same at any point in time.

Cloud Migration

Outdated systems and lack of internal IT-skills are just two of the many reasons to migrate to cloud platforms. But the prospect if the migration itself can be daunting. Our experts can support you in organizing and implementing of it, as they have done it many times.

For inspiration, feel free to browse through some of the highlighted  case studies below.