Sensors4Rail: Development of a machine learning operations pipeline

For the Sensors4Rail project, we have developed a MLOps pipeline together with our sister company Bosch Engineering GmbH, which creates a standardized and scalable solution for the entire machine learning toolchain.

A train pulling into the station with a person on a tablet checking the train's software

Challenge: High manual effort for large data volumes

Machine learning operations (MLOps) aims to reduce overhead as well as simplify and, if needed, automate manual tasks for existing and future projects. The growing volume of data also brings new challenges in cloud environments:

Infographic with simplified representation of the MLOps pipeline architecture.

Solution: Automation with MLOps

The MLOps pipeline provides automated deployment of scalable cloud infrastructure and workflow optimization for machine learning algorithm development. The process includes automated data ingestion, as well as configured tools for data exploration & selection, anonymization, labeling, dataset generation, ML training, and evaluation. Through data indexing, we enable efficient search capabilities for relevant data.

Our solution offers full traceability: versioning of ML code, datasets, ML models and linking among them. Another advantage is data selection using Active Learning, which improves the quality of ML models and reduces human workload.

Portrait von Heiko Mangold, Leiter Geschäftsfeld Bahntechnik, Bosch Engineering GmbH

For the Sensors4Rail project, we are taking ML development to a new level with a MLOps pipeline. Through standardized tools and scalability, we achieve huge cost and time savings when processing large amounts of data. Traceability integration ensures transparency and trustworthiness throughout the model lifecycle.

Heiko Mangold, Head of Business Unit Rail, Bosch Engineering GmbH

Added value: Optimization through future-ready MLOps pipeline

This MLOps pipeline has been developed as part of the Sensors4Rail research project, in which sensor-based systems have been tested for environment perception. The results of the Sensors4Rail project are expected to help to improve the efficiency, safety and reliability of rail vehicles and thus optimize the overall operation of rail networks.

These are precisely the goals to which the MLOps pipeline contributes. Its implementation will increase productivity for our customers and enable a continuous model improvement process. The pipeline offers extensive scalability and shorter development cycles. Another benefit is that the full potential of all available data is exploited. With this MLOps pipeline, we can thus improve efficiency and performance in railway engineering.

Key Takeaways

Icon Performancesteigerung

Increased productivity

Icon Variantenvielfalt

Shorter development cycles

Icon smarter Chip

Lower costs for machine learning applications

You also might be interested in this