Course V4.0 ASPICE – Machine Learning Engineering Process Group
Content
V4.0 ASPICE – Introducing Machine Learning (35 min)
- V4.0 Machine Learning (MLE) Characteristics
- V4.0 Machine Learning Connections
Machine Learning Requirements Analysis (MLE.1) (55 min)
- MLE.1 Purpose and Outcomes
- MLE.1 Base Practices and Output Information Items
- MLE.1 Output Information Items – Connections and Details
Machine Learning Architecture (MLE.2) (50 min)
- MLE.2 Purpose and Outcomes
- MLE.2 Base Practices and Output Information Items
- MLE.2 Output Information Items – Connections and Details
Machine Learning Training (MLE.3) (50 min)
- MLE.3 Purpose and Outcomes
- MLE.3 Base Practices and Output Information Items
- MLE.3 Output Information Items – Connections and Details
Machine Learning Model Testing (MLE.4) (55 min)
- MLE.4 Purpose and Outcomes
- MLE.4 Base Practices and Output Information Items
- MLE.4 Output Information Items – Connections and Details
Target
This e-learning provides a comprehensive introduction to the ASPICE V4.0 Machine Learning Engineering (MLE) Process Group, focusing on how machine learning processes integrate into the ASPICE framework. You’ll learn about the characteristics and connections of MLE within automotive software development and gain a solid understanding of each key process:
MLE.1 Requirements Analysis — understanding purpose, outcomes, and essential base practices for defining and managing ML requirements.
MLE.2 Architecture — exploring how to structure ML systems, identify crucial output information items, and ensure traceability.
MLE.3 Training — learning the foundational steps for developing and refining ML models through systematic, process-driven training.
MLE.4 Model Testing — examining how to validate, evaluate, and ensure the reliability of ML models within the ASPICE framework.
By the end of this course, you will have a clear understanding of how ASPICE V4.0 addresses machine learning processes, from requirements to testing, ensuring quality and compliance in AI-driven automotive systems.
Trailer
Insights
Course Content
What is the Machine Learning Engineering Process Group?
The engineering process group MLE was introduced with ASPICE 4.0. It consists of 4 processes MLE.1 Machine Learning Requirements Analysis, MLE.2 Machine Learning Architecture, MLE.3 Machine Learning Training and MLE.4 Machine Learning Model Testing.
The Machine Learning Engineering Process Group has a strong link to the process SUP.11 Machine Learning Data Management which was introduced newly as well.
Why is the Machine Learning Engineering Process Group Needed?
This process group is needed because machine learning introduces probabilistic behavior, data-driven development steps, and strong dependencies on data quality. Traditional engineering processes alone are not sufficient to ensure transparency, reproducibility, and safety when developing ML components. The MLE processes therefore help teams to systematically define ML requirements, design suitable architectures, control model training, and validate model performance in a way that meets automotive quality expectations.
How is Machine Learning Related to Embedded Systems?
Machine learning is increasingly integrated into embedded systems, where models interact directly with sensors, actuators, hardware, and safety-related functions. The MLE processes ensure that ML development is not isolated but aligned with system, software, and hardware engineering, making it possible to safely integrate ML components into vehicles and other embedded platforms.
What Can You Learn About ASPICE in the Embedded Academy course on Machine Learning?
In the Embedded Academy E-Learning, learners receive a structured introduction to ASPICE Machine Learning, including ML characteristics, connections to existing ASPICE processes, and detailed guidance through all four MLE processes. The course explains the purpose, outcomes, base practices, and required work products of MLE.1 to MLE.4, equipping engineers with the knowledge needed to develop and assess ML components in ASPICE-compliant automotive projects.
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