MLE.3 Machine Learning Training#

The purpose is to optimize the ML model to meet the defined ML requirements.

Process outcomes#

A ML training and validation approach is specified.
The data set for ML training and ML validation is created.
The ML model, including hyperparameter values, is optimized to meet the defined ML requirements.
Consistency and bidirectional traceability are established between the ML training and validation data set and the ML data requirements.
Results of optimization are summarized, and the trained ML model is agreed and communicated to all affected parties.

MLE.3.BP1: Specify ML training and validation approach		status: valid
tags: aspice40_mle3 links outgoing: std_req__aspice_40__iic-08-65
Specify an approach which supports the training and validation of the ML model to meet the defined ML requirements. The ML training and validation approach includes entry and exit criteria of the training and validation, approaches for hyperparameter tuning / optimization, approach for data set creation and modification, and training and validation environment Note The ML training and validation approach may include random dropout and other robustification methods. Note ML validation is the optimization of the hyperparameters during Machine Learning Training (MLE.3). The term “validation” has a different meaning than VAL.1. Note The training environment should reflect the environment of the deployed model.
std_req__aspice_40__MLE-3-BP1		Standard Requirement

MLE.3.BP2: Create ML training and validation data set		status: valid
tags: aspice40_mle3 links outgoing: std_req__aspice_40__iic-03-51
Select data from the ML data collection provided by SUP.11 and assign them to the data set for training and validation of the ML model according to the specified ML training and validation approach. Note The ML training and validation data set may include corner cases, unexpected cases, and normal cases depending on the ML requirements. Note A separated data set for training and validation might not be required in some cases (e.g., k-fold cross validation, no optimization of hyperparameters).
std_req__aspice_40__MLE-3-BP2		Standard Requirement

MLE.3.BP3: Create and optimize ML model		status: valid
tags: aspice40_mle3 links outgoing: std_req__aspice_40__iic-01-53, std_req__aspice_40__iic-01-54
Create the ML model according to the ML architecture and train it, using the identified ML training and validation data set according to the ML training and validation approach to meet the defined ML requirements, and training and validation exit criteria.
std_req__aspice_40__MLE-3-BP3		Standard Requirement

MLE.3.BP4: Ensure consistency and establish bidirectional traceability		status: valid
tags: aspice40_mle3 links outgoing: std_req__aspice_40__iic-13-51
Ensure consistency and establish bidirectional traceability between the ML training and validation data set and the ML data requirements. Note Bidirectional traceability supports consistency and facilitates impact analyses of change requests. Traceability alone, e.g., the existence of links, does not necessarily mean that the information is consistent with each other.
std_req__aspice_40__MLE-3-BP4		Standard Requirement

MLE.3.BP5: Summarize and communicate agreed trained ML model		status: valid
tags: aspice40_mle3 links outgoing: std_req__aspice_40__iic-13-52
Summarize the results of the optimization and inform all affected parties about the agreed trained ML model.
std_req__aspice_40__MLE-3-BP5		Standard Requirement