Transparency allows modellers, developers, and technical auditors to understand how an AI system works, including how a model is trained and evaluated, what its decision boundaries are, what inputs go into the model, and finally, why it made a specific prediction. This is often also described as ...

• The shift from explanations to predictions defines predictive AI, yet generative AI challenges this by creating data-driven insights that go beyond explanation or prediction into transformation.
• For example, instead of explaining huma...

To develop and manage a production-ready model, you must work through the following stages:

• Source and prepare your data.
• Develop your model.

• Train an ML model on your data:

• Train model

• Evaluate model accuracy

• Tune hyper...