Providing deep learning models as webservices

Setting up a webservice with a machine learning pipelines in the backend to let users/customers/project partners:

  • Manage Datasets, Annotations and Models
  • Execution of Machine Learning Applications, Training and Inference
  • Access to high-performance compute cluster via simple user interface
  • Annotation integration


machine learning pipelines, workflows, datastorage

Combination of software architecture, microservices and tools to use when creating ML pipelines

docker, kubernetes, airflow, minio

concret: specific best practice (e.g. use microservice)

Application domain:
Data science (analysis & visualisation), Software engineering

Main phase:
Data Science: Preparation/Integration, Data Science: Modeling/Training/Evaluation, Development: Implementation/Code/Build, Development: Testing

Related literature:;;

In which projects do/did you use this practice?
S3AI, FlexSpect,...


0–2 years of experiences
Software Competence Center Hagenberg

1. How do ​you rate the potential benefit for your projects? 5
2. How often are you using that practice? 4
3. What is the effort to introduce the practice in your project upfront? 3
4. What is the effort to apply the best practice in your project daily basis? 3

Questions 1, 3 and 4 (1 = Low, 5 = High)
Question 2 (1 = Never, 5 = Always)

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