About Us

ML AI Consulting

Our Background

On the one hand, we are part of the successful Swiss Digital Network and leverage our background in managing highly reliable software and infrastructure projects and democratising established and proven concepts such as the “Digital Highway for Continuous Software Delivery” and “Effective Site Reliability Engineering (SRE)” in the Swiss market. On the other hand, we have combined this expertise, with new team members experienced in Data Science and Machine Learning and building solutions for customers to digitise and automate processes. 

Our Team

Taking advantage of our diverse backgrounds and experiences we are forming a polyvalent consulting team of:

  • Data Scientists,
  • Data & Machine Learning Engineers,
  • Software Developers & Site Reliability Engineers (SRE),
  • Domain experts (Life Scientists, IT Architects, etc.).

Our Approach

We are partnering with innovative and leading technology providers (Spark/Databricks, Druid/Imply, etc.) and we are supporting organisations of different sizes and from different industries in their Data Analytics, AI and Machine Learning initiatives.

Our Services

👉 Digitalise and automate processes

👉 Design and build unified analytics platforms and Machine Learning systems

👉 Train teams and individuals with dedicated skills in Data Science for Life Sciences (DSfLS) and Site Reliability Engineering (SRE)

Our Results

✅ Self-service analytics capabilities and operating models

✅ End-to-end Machine Learning implementations, incl. deployment and operations (MLOps) of AI solutions

✅ Enablement of non-technical (e.g. life scientists) and technical (IT) people in Data Science for their domain and SRE for DevOps/MLOps

MLAB Portfolio Overview


Individual Machine Learning Implementations, Operating Models, Architecture Designs, Engineering Expertise and for MLOps

  • Individual and customised Machine Learning solutions to digitalise and automate tasks and processes
  • Maturity assessments and roadmaps to manage governance, processes and tools
  • MLOps and self-service analytics architecture designs for end-to-end ML system development (Dev) and ML system operation (Ops)
  • Engineering expertise to build Analytics, Data Science and MLOps workspaces and tool chains
  • Continuous delivery, observability and model performance management support and guidance to leverage DevOps and Site Reliability Engineering (SRE) principles for Machine Learning

Data Science, Machine Learning, Self-Service & Real-time Analytics Technologies for MLOps

  • Customer specific and adapted technology evaluations and roll-outs of:
    • Effective MLOps for end-to-end ML system development (Dev) and ML system operation (Ops)
    • Continuous Delivery tooling
    • Self-service and real-time analytics for data insights and operations monitoring
  • Objective and up-to-date benchmarks of both established market solutions (e.g. Gartner Magic Quadrant for Data Science and Machine Learning) and new, innovative (next generation) tools
technology setup

Data Science & Site Reliability Engineering (SRE) Trainings for Effective MLOps

  • Domain specific offerings of comprehensive, local and cost-effective Data Science trainings for:

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Engineering – Architecture – Consulting – Implementation – Training