First we clearly define the objectives and goals of the Easy-B' neural engine. This helps to understand the problem we aims to solve or the tasks Easy-B should perform. The quality and quantity of data collection are crucial for effective training. Then we extract and select relevant features from the data set that will be used as input for Easy-B. For supervised learning, we label the data, indicating the correct output for each input. This step is essential for our AI model to learn from labeled examples.

In the next step we select an appropriate AI model architecture based on your requirements. Common architectures include neural networks for deep learning. Then we define a loss function that measures the difference between the model's predictions and the actual labels. We choose an optimization algorithm (optimizer) to adjust the model's parameters during training. Common optimizers include stochastic gradient descent (SGD) and different variants. After that we feed the training data into the model, compute the loss, and use backpropagation to update the model's parameters iteratively.

Before deploying we adjust hyperparameters, such as learning rate and regularization, based on the validation performance to optimize the model. In a final step we assess the final model's performance on the testing set, providing an unbiased evaluation of its capabilities. Once satisfied with the Easy-B's performance, we deploy it for your real-world applications, where it can make predictions or automate tasks based on new, unseen data.