[Doc] Fixing typos in diverse files

Document/content/1.2_Principles_of_AI_Testing.md

@@ -67,7 +67,7 @@ ISO/IEC 23053 \[4\] structures the ML-based AI system lifecycle into a series of
 
 1. **Planning & Scoping:** In this phase, you establish clear business objectives, success metrics, and ML use cases while identifying key stakeholders, regulatory requirements, and the organization’s risk tolerance.  
 2. **Data Preparation:** In this phase, you gather and document raw data sources, conduct profiling and quality checks through preprocessing pipelines, and implement versioning and lineage tracking for full data traceability.  
-3. **Model Development & Training:** In this  phase, you choose appropriate algorithms and architectures, train models on curated datasets with feature engineering, and record experiments, including the parameters that govern the learning process (i.e hyperparameters) and performance metrics in a model registry.  
+3. **Model Development & Training:** In this  phase, you choose appropriate algorithms and architectures, train models on curated datasets with feature engineering, and record experiments, including the parameters that govern the learning process (i.e. hyperparameters) and performance metrics in a model registry.  
 4. **Validation & Evaluation:** in this phase, you test models using reserved and adversarial datasets, perform fairness, robustness, and security evaluations, and ensure they meet functional, ethical, and regulatory standards.  
 5. **Deployment & Integration:** in this phase, you are preparing and bundling your trained AI model into a deployable artifact for either service (i.e. wrap the model in a microservice or API) or edge deployment (i.e. convert and optimize the model for resource-constrained devices such as IoT gateways or mobile phones)  automate build-test-release workflows via CI/CD, and verify infrastructure security measures   
 6. **Operation & Maintenance:** in this phase while the AI product is in production environment, you will continuously monitor performance, data drift, and audit logs, triggering alerts on anomalies or compliance breaches, while periodically retraining models with fresh data, re-validating security, privacy, and fairness controls, and updating documentation, training, and policies as needed.