Our AI Methodology
At Optimise AI, we utilise a patented methodology to ensure the highest standards while developing our AI models. Our approach is designed to deliver reliable and accurate solutions tailored to specific needs. Below is a summary of our comprehensive AI methodology:
Data Pre-processing
Data Pre-processing
Clean the data by handling missing values and gaps, detecting anomalies and duplicates, and data imputation. Transform the data (normalisation, encoding categorical variables) to enhance data integrity and homogeneity.
Clean the data by handling missing values and gaps, detecting anomalies and duplicates, and data imputation. Transform the data (normalisation, encoding categorical variables) to enhance data integrity and homogeneity.
Selection of machine learning method
Selection of machine learning method
Choose the appropriate AI methodology based on the problem and data types (i) Supervised Learning for labelled data, (ii) Unsupervised Learning for finding patterns in unlabelled data, (iii) Reinforcement Learning for decision-making in dynamic environments.
Choose the appropriate AI methodology based on the problem and data types (i) Supervised Learning for labelled data, (ii) Unsupervised Learning for finding patterns in unlabelled data, (iii) Reinforcement Learning for decision-making in dynamic environments.
Model Selection
Model Selection
Select specific algorithms or models that fit the chosen methodology. Consider factors like complexity, interpretability, and performance.
Select specific algorithms or models that fit the chosen methodology. Consider factors like complexity, interpretability, and performance.
Model Training
Model Training
Integrate and split the data into different subsets for training, testing, and validation procedures. Train the model using the training dataset, adjusting parameters and optimising performance.
Integrate and split the data into different subsets for training, testing, and validation procedures. Train the model using the training dataset, adjusting parameters and optimising performance.
Model Evaluation
Model Evaluation
Evaluate the model's performance using metrics relevant to the problem (e.g., accuracy, precision, recall, F1 score). Use the validation dataset to assess how well the model generalises to unseen data.
Evaluate the model's performance using metrics relevant to the problem (e.g., accuracy, precision, recall, F1 score). Use the validation dataset to assess how well the model generalises to unseen data.
Model Evaluation
Model Evaluation
Before deployment, models undergo a stringent approval process to ensure they meet our quality standards:
Accuracy Threshold: Models must achieve a minimum accuracy of 75% to be considered for approval.
Multi-Metric Assessment: In addition to accuracy, we evaluate other metrics (e.g., precision, recall) to ensure balanced performance.
Contextual Evaluation: Depending on the application, higher accuracy thresholds may be set to meet specific industry standards or client requirements.
Peer Review: Approved models are reviewed by our data science team to validate results and ensure robustness.
Before deployment, models undergo a stringent approval process to ensure they meet our quality standards:
Accuracy Threshold: Models must achieve a minimum accuracy of 75% to be considered for approval.
Multi-Metric Assessment: In addition to accuracy, we evaluate other metrics (e.g., precision, recall) to ensure balanced performance.
Contextual Evaluation: Depending on the application, higher accuracy thresholds may be set to meet specific industry standards or client requirements.
Peer Review: Approved models are reviewed by our data science team to validate results and ensure robustness.
Hyperparameter Tuning
Hyperparameter Tuning
Optimise the model by adjusting hyperparameters to improve performance. Techniques include grid search, random search, or using automated tools.
Optimise the model by adjusting hyperparameters to improve performance. Techniques include grid search, random search, or using automated tools.
Semantic Calibration
Semantic Calibration
Use feedback from monitoring to refine the model and improve outcomes. Iterate through the steps as needed to enhance performance and address new challenges.
Use feedback from monitoring to refine the model and improve outcomes. Iterate through the steps as needed to enhance performance and address new challenges.
Copyright ©
2024
optimise-ai.com
Copyright ©
2024
optimise-ai.com
Copyright ©
2024
optimise-ai.com