chore: cleanup orphaned content files (#9948)

Co-authored-by: jcanalesluna <56018501+jcanalesluna@users.noreply.github.com>

src/data/roadmaps/mlops/content/airflow@pVSlVHXIap0unFxLGM-lQ.md

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-# Airflow
-
-Airflow is a platform to programmatically author, schedule, and monitor workflows. Use airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command-line utilities make performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress, and troubleshoot issues when needed. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
-
-Visit the following resources to learn more:
-
-- [@official@Airflow](https://airflow.apache.org/)
-- [@official@Airflow Documentation](https://airflow.apache.org/docs)
-- [@feed@Explore top posts about Apache Airflow](https://app.daily.dev/tags/apache-airflow?ref=roadmapsh)

src/data/roadmaps/mlops/content/data-lineage--feature-stores@6XgP_2NLuiw654zvTyueT.md

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-# Data Lineage and Feature Stores
-
-**Data Lineage** refers to the life-cycle of data, including its origins, movements, characteristics and quality. It's a critical component in MLOps for tracking the journey of data through every process in a pipeline, from raw input to model output. Data lineage helps in maintaining transparency, ensuring compliance, and facilitating data debugging or tracing data-related bugs. It provides a clear representation of data sources, transformations, and dependencies, thereby aiding in audits, governance, or reproduction of machine learning models.
-
-Visit the following resources to learn more:
-
-- [@article@What is Data Lineage?](https://www.ibm.com/topics/data-lineage)
-- [@article@What is a Feature Store](https://www.snowflake.com/guides/what-feature-store-machine-learning/)

src/data/roadmaps/mlops/content/experiment-tracking--model-registry@fGGWKmAJ50Ke6wWJBEgby.md

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-# Experiment Tracking and Model Registry
-
-**Experiment Tracking** is an essential part of MLOps, providing a system to monitor and record the different experiments conducted during the machine learning model development process. This involves capturing, organizing and visualizing the metadata associated with each experiment, such as hyperparameters used, models produced, metrics like accuracy or loss, and other information about the computational environment. This tracking allows for reproducibility of experiments, comparison across different experiment runs, and helps in identifying the best models.
-
-Visit the following resources to learn more:
-
-- [@article@Experiment Tracking](https://madewithml.com/courses/mlops/experiment-tracking/#dashboard)
-- [@article@ML Flow Model Registry](https://mlflow.org/docs/latest/model-registry.html)

src/data/roadmaps/mlops/content/graphana@YmRN43yAFF97c7MgNaiqs.md

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-# Grafana
-
-Grafana is an open-source data visualization and monitoring tool. It allows users to query, visualize, alert on, and explore metrics, logs, and traces. Grafana connects to various data sources, such as Prometheus, Graphite, Elasticsearch, and InfluxDB, to create customizable dashboards that display real-time data and historical trends.
-
-Visit the following resources to learn more:
-
-- [@official@Grafana](https://grafana.com/)
-- [@official@Grafana Docs](https://grafana.com/docs/)
-- [@official@Grafana Webinars and Videos](https://grafana.com/videos/)
-- [@article@What is Grafana?](https://www.redhat.com/en/topics/data-services/what-is-grafana)
-- [@video@Grafana Explained in Under 5 Minutes ⏲](https://www.youtube.com/watch?v=lILY8eSspEo)
-- [@video@Grafana for Beginners Ep. 1](https://www.youtube.com/watch?v=TQur9GJHIIQ&list=PLDGkOdUX1Ujo27m6qiTPPCpFHVfyKq9jT)

src/data/roadmaps/mlops/content/version-control-systems@oUhlUoWQQ1txx_sepD5ev.md

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-# Version Control Systems
-
-Version control systems are tools that manage changes to code, documents, and other files over time. They allow multiple people to collaborate on a project without overwriting each other's work, track the history of changes, and revert to previous versions if needed. These systems essentially create a detailed record of modifications, enabling efficient collaboration and easier debugging.
-
-Visit the following resources to learn more:
-
-- [@official@Git](https://git-scm.com/)
-- [@article@What is Version Control?](https://www.atlassian.com/git/tutorials/what-is-version-control)
-- [@feed@Explore top posts about Version Control](https://app.daily.dev/tags/version-control?ref=roadmapsh)