Which of the following are examples of pipeline monitoring solutions?

Pipeline monitoring solutions are essential for managing and overseeing the various stages of data science workflows. They allow practitioners to ensure that data processing, modeling, and deployment are executed smoothly and that any issues can be identified and resolved promptly.

The correct choice includes tools specifically designed for monitoring and managing machine learning workflows and data pipelines. Kubeflow and MLflow are both focused on the machine learning lifecycle, providing capabilities for tracking experiments, managing models, and deploying them efficiently. Datadog, on the other hand, is a monitoring and analytics platform that can be used to oversee infrastructure and applications, ensuring that the entire data pipeline operates correctly and efficiently.

In contrast, the other options consist of tools that serve different purposes. TensorFlow, Keras, and PyTorch are primarily frameworks for building and training machine learning models, not for pipeline monitoring. Apache Spark, Hadoop, and Hive are big data processing frameworks focused on data analysis and processing rather than monitoring pipelines. Jupyter Notebook, RStudio, and Tableau are primarily used for data analysis, visualization, and interactive computing, lacking the specific monitoring features that are integral to effective pipeline oversight.