Which two activities are key components of the machine learning development process?

In the machine learning development process, experimentation and training operationalization are fundamental activities. Experimentation involves testing different model architectures, hyperparameters, and algorithms to find the best-performing model for a given task. This phase is crucial because it allows engineers to iterate on their solutions, examine their models' behaviors, and make informed decisions based on empirical results.

Training operationalization refers to the process of taking the best-performing model and ensuring it can be efficiently trained and deployed within a production environment. This includes setting up the proper infrastructure, integrating the model with application platforms, and addressing considerations such as scalability and reproducibility.

These two components together encompass critical aspects of the development lifecycle, ensuring that models are not only created but also effectively translated into deliverable technology that fulfills business needs.