# Blockchain Secured Dynamic Machine Learning Pipeline for Manufacturing
## 5 Proposed Method for Mitigation
### Sidechain
#### 5.4.1 Create Birth Certificates  
After each stakeholder within the blockchain has been made traceable through certificates and trust management, two further important components of this machine learning pipeline need to be secured. These two components are the ML models and their creation, and the machine learning pipeline (components, software, configurations) itself. The aim is to make all software and its configuration involved in the ML pipeline traceable.  
Machine Learning Model Birth Certificates  
The Machine Learning Model Birth Certificate stores all relevant information that allows conclusions to be drawn about its creation. These are, as described in more detail in a previous paper [16], information about the model life cycle phases (1. Model Requirements, 2. Data Collection, 3. Data Cleaning, 4. Data Labeling, 5. Feature Engineering, 6. Model Training, 7. Model Evaluation, 8. Model Deployment, 9. Model Monitoring) [50].  
The most important information for each phase are presented below:  
1. Model Requirements: Goal of the model, decision made to achieve the model goal.  
Figure 3: Sidechain Process.  
Data Collection: Information about data acquisition, sensor model, environment parameter.
* Data Cleaning: Description of the cleaning method, parameters and software.
* Data Labelling: Description of the labelling method, instructions, software and person who carried out the labelling.
* Feature Engineering: Method and parameters of data augmentation.
* Model Training: Hyperparameters, etc.
* Model Evaluation: Accuracy, precision, etc.
* Model Deployment: Information about machine learning pipeline (described in more detail later).
* Model Monitoring: Alert threshold.  
These machine learning model birth certificates are stored in the blockchain and contain a reference to the certificate of the creator of each phase.  
Machine Learning Pipeline Birth Certificates  
As with machine learning model birth certificates, a birth certificate is created for the machine learning pipeline that includes the architecture, the structure and processes within the individual components, software versions and configurations to make it traceable; the selection of this information is justified below.  
* The architecture of the ML pipeline determines the order of execution of the individual components. Since the components change the state of the data, a change in the order results in a different outcome.
* Certain pipeline architectures are to be considered invalid. Determined results are therefore also to be considered invalid.  
* As with the entire ML pipeline, the order in which the subcomponents are executed must also be known in the components, since a change in order changes the state of the data.
* Since the components change the state of the data, it must be known which processes take place in these components.
* Software versions:
* It is important for the traceability in case of errors in the ML pipeline to be able to determine afterwards which software versions were used.
* If it is known which software versions are used in the ML pipeline, it is possible to react more quickly to known errors in the software by updating.  
* Software usually has configuration options. If this configuration is changed from the standard configuration, unintentional errors may occur in certain cases.
* Saving the software versions can be used not only to find errors, but also to quickly update the configuration in case of known errors.