Problem Definition:
Clearly defining the problem that needs to be solved. This includes identifying the specific task that the AI application will perform and the desired outcome.
Data Collection:
The next stage involves collecting and organizing data that will be used to train the AI model. This data would be representative of the problem being solved and of sufficient quality to produce accurate results.
Data Preprocessing:
After the data is collected, it will be preprocessed to clean and prepare it for use in training the AI model. This involves tasks such as data normalization, feature selection, and outlier detection.
Model Selection:
The AI model that will be used to solve the problem must be selected based on the specific task and the available data. This can involve choosing between different types of models such as neural networks, decision trees, or support vector machines.
Model Training:
The AI model is trained using the preprocessed data. This involves feeding the data into the model and adjusting the model's parameters to optimize its performance.
Model Evaluation:
Once the model is trained, it will be evaluated to determine its accuracy and effectiveness in solving the problem. This is typically done by testing the model on a separate set of data that was not used in the training process.
Model Deployment:
After the model is evaluated and found to be effective, it would be deployed for use in the real world. This may involve integrating the model into an existing system or building a new application around the model.
Model Monitoring & Maintenance:
Once the AI application is deployed, it would be monitored and maintained to ensure that it continues to produce accurate and effective results. This may involve periodic updates to the model or changes to the data used to train it.
