This opportunity is based in Lausanne

MLOps: scaling ML in an industrial setting (Internship / Diploma Thesis)


Virtually all industries are adopting or investigating machine learning (ML) to benefit their business. The primary purpose of most ML initiatives is to convert insight into business value (whether it is by recommendations, optimizations, fault detections etc.). The tighter the execution of this cycle, the quicker the business can respond to changing circumstances. Although sophisticated ML algorithms and many data scientist tools exist, putting ML into production (and continuously integrating new retraining) is still a challenge since it requires integrated discipline and practice between Operations, Data Science, and sometimes Business Analysts. MLOps (a compound of ML and DevOps) is a new concept/practice where ML technologies can generate business benefits by rapidly, frequently and reliably building, testing, and releasing ML technology into production (Continuous Integration/Continuous Development).

Challenges: Design the pipeline and orchestrate the different open source tools around ML or combine DevOps with ML development.

Project applications: Any ML project in an industrial setting

What you will learn: You will be a junior computer scientist/data scientist, developing your skills in machine learning in developing a E2E prototype pipeline from model building to production.

Possible extensions (depends on advancement of the work): mixed on-premise/cloud or entirely cloud solution (Azure ML pipeline or AWS SageMaker).

Keywords: CI/CD, DevOps

In this role

In this project, the goal is to:

  • Take an ML project and develop a protoptype pipeline that includes sourcing and cleansing of data, model building (with different parameters and algorithms), model and data versioning, model repo, model building environment, CI/CD pipeline, model interface and monitoring tool to detect data drifts.

What we offer

Join our team as intern and you will find a young, dynamic and culturally diverse working environment.

About your profile

knowledge / skills required  

  • Required: Jupyterhub, Docker, Kubernetes, Jenkins, Elastic or other NoSQL db
  • Software engineering: Python, shell scripting

If you are INTERESTED in applying for this position, please send us your complete application (CV, cover letter, letter of reference, diplomas and certificates).

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