Project Systems Development

Automated Machine Learning

You will be participating a leading-edge research project in the area of artificial intelligence (AI), in particular machine learning (ML). ML is a hype topic. But developing effective ML applications is complex, almost like a secret art. It requires experienced computer scientists or data scientists who analyse a dataset in detail and perform steps such as data preparation, feature engineering, model selection, hyperparameter optimisation and validation so that an ML application can make good predictions for new cases. 

Automated Machine Learning (AutoML) is an active research field that tries to automate some of those steps. There are now many AutoML solutions, both commercial and open source. They differ greatly in terms of functionality, maturity and user-friendliness. What they all have in common, however, is that they each focus on one or only a few selected ML libraries; commercial solutions each focus on the company's own ecosystem. This means that there is always a certain vendor lock-in when choosing an AutoML solution.

To counteract this, we are developing OMA-ML - Effective Machine Learning made easy! We use an ontology to condense knowledge about different AutoML solutions and strategies. In this way, we combine knowledge-based AI with ML - also one of the current trends in AI research.

https://oma-ml.ai 

OMA-ML is open source under GitHub https://github.com/hochschule-darmstadt/MetaAutoML  

Technologies:

• Backend: Python, rdflib and various ML libraries, Docker
• Frontend: C#, Web Framework Blazor
• Communication: gRPC
• Persistence: MongoDB

For details see the following publication:

Bernhard G. Humm, Alexander Zender: An Ontology-Based Concept for Meta AutoML. In I. Maglogiannis et al. (Eds.): Artificial Intelligence Applications and Innovations (AIAI 2021), IFIP AICT 627, pp. 117–128, IFIP International Federation for Information Processing 2021, published by Springer Nature Switzerland AG 2021. https://doi.org/10.1007/978-3-030-79150-6_10

Moodle course  enrolment key: PSE_Humm