Which OCI Data science feature allows for defining and executing repeatable machine learning tasks?

Which OCI Data science feature allows for defining and executing repeatable machine learning tasks?

• A. Workflows
• B. Jobs
• C. Pipelines
• D. Tasks

Answer: (B)

The feature that allows for defining and executing repeatable machine learning tasks in Oracle Cloud Infrastructure (OCI) Data Science is referred to as Pipelines. This capability is designed to simplify the workflow of data scientists by enabling them to create a sequence of interrelated steps that represent the entire machine learning process. Pipelines facilitate the automation of machine learning workflows, ensuring that tasks can be executed in a defined order, making it easier to manage and reproduce experiments. By utilizing this feature, data scientists can streamline their processes for training models, tuning parameters, and deploying solutions in a systematic and repeatable manner. It allows teams to maintain consistency and control over their machine learning workflows, which is essential in a collaborative environment. In contrast, while Jobs are used for executing tasks within the OCI ecosystem, they do not inherently encapsulate the repeatable nature of a complete machine learning workflow. Workflows and Tasks serve different purposes within the data science stack. Hence, Pipelines stands out as the correct choice for managing repeatable machine learning tasks effectively.

The feature that allows for defining and executing repeatable machine learning tasks in Oracle Cloud Infrastructure (OCI) Data Science is referred to as Pipelines. This capability is designed to simplify the workflow of data scientists by enabling them to create a sequence of interrelated steps that represent the entire machine learning process.

Pipelines facilitate the automation of machine learning workflows, ensuring that tasks can be executed in a defined order, making it easier to manage and reproduce experiments. By utilizing this feature, data scientists can streamline their processes for training models, tuning parameters, and deploying solutions in a systematic and repeatable manner. It allows teams to maintain consistency and control over their machine learning workflows, which is essential in a collaborative environment.

In contrast, while Jobs are used for executing tasks within the OCI ecosystem, they do not inherently encapsulate the repeatable nature of a complete machine learning workflow. Workflows and Tasks serve different purposes within the data science stack. Hence, Pipelines stands out as the correct choice for managing repeatable machine learning tasks effectively.