Which two software components are essential in the AI development and deployment life cycle for model training and serving?

The correct choice identifies Kubeflow and MLflow as essential software components in the AI development and deployment life cycle, specifically for model training and serving.

Kubeflow is a powerful tool designed to facilitate the development, orchestration, deployment, and running of scalable and portable ML workloads on Kubernetes. It provides a robust platform that simplifies the complexities of managing the end-to-end machine learning (ML) workflow. This includes capabilities such as model training, where developers can leverage Kubernetes' container orchestration to automate various stages of the ML process.

MLflow complements this by providing a platform to manage the ML lifecycle, including experimentation, reproducibility, and deployment. It helps data scientists and engineers track their experiments, register models, and transition models seamlessly to production. Together, Kubeflow and MLflow create a cohesive environment that supports both the training of models and their subsequent serving in production.

Other options, while they include software utilized in data processing or model building, do not provide the focused suite of features that are essential for both training and serving in an integrated manner. For instance, Apache Spark is primarily a data processing framework and not specifically tailored for the ML lifecycle in the same comprehensive way as Kubeflow and MLflow. Similarly, TensorFlow and