Which steps belong to the TensorFlow development process?

The TensorFlow development process includes several key steps that are essential for building and deploying machine learning models effectively. Among these steps, model restoration and invoking play a crucial role.

In the context of TensorFlow, model restoration refers to loading a previously trained model or checkpoint, which allows developers to resume training or make predictions without having to retrain the model from scratch. This step is critical for efficiency, particularly in scenarios where training on large datasets or complex models requires significant computational resources.

Invoking the model involves using it to perform inference on new data, which is the ultimate goal of developing any machine learning model. After a model has been trained and potentially fine-tuned, it needs to be applied to real-world scenarios, and invoking facilitates this application.

While model testing and evaluation, data analysis, and model visualization are important steps in the overall machine learning workflow, they do not specifically pertain to the direct processes of restoring and running a model after it has been created. Instead, they help in the preparation, assessment, and understanding of the models throughout their lifecycle.

Thus, the focus on model restoration and invoking is critical as it ensures that the developed models can be efficiently retrained and applied to new data, marking essential elements of the TensorFlow development process