MLflow: Refactor section to dedicated page

docs/integrate/index.md

@@ -47,6 +47,7 @@ marquez/index
 meltano/index
 metabase/index
 mindsdb/index
+mlflow/index
 mongodb/index
 mqtt/index
 mysql/index

docs/integrate/mlflow/index.md

@@ -0,0 +1,78 @@
+(mlflow)=
+# MLflow
+
+```{div}
+:style: "float: right; margin-left: 1em"
+[![MLflow logo](https://github.com/crate/crate-clients-tools/assets/453543/d1d4f4ac-1b44-46b8-ba6f-4a82607c57d3){height=60px loading=lazy}][MLflow]
+```
+```{div}
+:style: "clear: both"
+```
+
+:::{rubric} About
+:::
+
+[MLflow] is an open-source platform to manage the whole ML lifecycle, including
+experimentation, reproducibility, deployment, and a central model registry.
+
+The [MLflow adapter for CrateDB], available through the [mlflow-cratedb] package
+on PyPI, provides support to use CrateDB as a storage database for the
+[MLflow Tracking] subsystem, which is about recording and querying experiments,
+across code, data, config, and results.
+
+:::{rubric} Learn
+:::
+Tutorials and Notebooks about using [MLflow] together with CrateDB.
+
+::::{info-card}
+:::{grid-item}
+:columns: 9
+**Blog: Running Time Series Models in Production using CrateDB**
+
+Part 1: Introduction to [Time Series Modeling using Machine Learning]
+
+The article will introduce you to the concept of time series modeling,
+discussing the main obstacles running it in production.
+It will introduce you to CrateDB, highlighting its key features and
+benefits, why it stands out in managing time series data, and why it is
+an especially good fit for supporting machine learning models in production.
+:::
+:::{grid-item}
+:columns: 3
+{tags-primary}`Fundamentals` \
+{tags-secondary}`Time Series Modeling`
+:::
+::::
+
+
+::::{info-card}
+:::{grid-item}
+:columns: 9
+**Notebook: Create a Time Series Anomaly Detection Model**
+
+Guidelines and runnable code to get started with MLflow and
+CrateDB, exercising time series anomaly detection and time series forecasting /
+prediction using NumPy, Salesforce Merlion, and Matplotlib.
+
+[![README](https://img.shields.io/badge/Open-README-darkblue?logo=GitHub)][MLflow and CrateDB]
+[![Notebook on GitHub](https://img.shields.io/badge/Open-Notebook%20on%20GitHub-darkgreen?logo=GitHub)][tracking-merlion-github]
+[![Notebook on Colab](https://img.shields.io/badge/Open-Notebook%20on%20Colab-blue?logo=Google%20Colab)][tracking-merlion-colab]
+:::
+:::{grid-item}
+:columns: 3
+{tags-primary}`Fundamentals` \
+{tags-secondary}`Time Series` \
+{tags-secondary}`Anomaly Detection` \
+{tags-secondary}`Prediction / Forecasting`
+:::
+::::
+
+
+[MLflow]: https://mlflow.org/
+[MLflow adapter for CrateDB]: https://github.com/crate/mlflow-cratedb
+[MLflow and CrateDB]: https://github.com/crate/cratedb-examples/tree/main/topic/machine-learning/mlflow
+[mlflow-cratedb]: https://pypi.org/project/mlflow-cratedb/
+[MLflow Tracking]: https://mlflow.org/docs/latest/tracking.html
+[Time Series Modeling using Machine Learning]: https://cratedb.com/blog/introduction-to-time-series-modeling-with-cratedb-machine-learning-time-series-data
+[tracking-merlion-colab]: https://colab.research.google.com/github/crate/cratedb-examples/blob/main/topic/machine-learning/mlflow/tracking_merlion.ipynb
+[tracking-merlion-github]: https://github.com/crate/cratedb-examples/blob/main/topic/machine-learning/mlflow/tracking_merlion.ipynb

docs/topic/ml/index.md

@@ -58,74 +58,11 @@ generation (RAG), and other applications.
 ## Anomaly Detection and Forecasting
 
 
-(mlflow)=
 ### MLflow
-
-:::{rubric} About
-:::
-```{div}
-:style: "float: right; margin-left: 1em"
-[![](https://github.com/crate/crate-clients-tools/assets/453543/d1d4f4ac-1b44-46b8-ba6f-4a82607c57d3){w=180px}](https://mlflow.org/)
-```
-
-[MLflow] is an open source platform to manage the whole ML lifecycle, including
-experimentation, reproducibility, deployment, and a central model registry.
-
-The [MLflow adapter for CrateDB], available through the [mlflow-cratedb] package,
-provides support to use CrateDB as a storage database for the [MLflow Tracking]
-subsystem, which is about recording and querying experiments, across code, data,
-config, and results.
-
-```{div}
-:style: "clear: both"
-```
-
-:::{rubric} Learn
-:::
-Tutorials and Notebooks about using [MLflow] together with CrateDB.
-
-::::{info-card}
-:::{grid-item}
-:columns: 9
-**Blog: Running Time Series Models in Production using CrateDB**
-
-Part 1: Introduction to [Time Series Modeling using Machine Learning]
-
-The article will introduce you to the concept of time series modeling,
-discussing the main obstacles running it in production.
-It will introduce you to CrateDB, highlighting its key features and
-benefits, why it stands out in managing time series data, and why it is
-an especially good fit for supporting machine learning models in production.
-:::
-:::{grid-item}
-:columns: 3
-{tags-primary}`Fundamentals` \
-{tags-secondary}`Time Series Modeling`
-:::
-::::
-
-
-::::{info-card}
-:::{grid-item}
-:columns: 9
-**Notebook: Create a Time Series Anomaly Detection Model**
-
-Guidelines and runnable code to get started with MLflow and
-CrateDB, exercising time series anomaly detection and time series forecasting /
-prediction using NumPy, Salesforce Merlion, and Matplotlib.
-
-[![README](https://img.shields.io/badge/Open-README-darkblue?logo=GitHub)][MLflow and CrateDB]
-[![Notebook on GitHub](https://img.shields.io/badge/Open-Notebook%20on%20GitHub-darkgreen?logo=GitHub)][tracking-merlion-github]
-[![Notebook on Colab](https://img.shields.io/badge/Open-Notebook%20on%20Colab-blue?logo=Google%20Colab)][tracking-merlion-colab]
-:::
-:::{grid-item}
-:columns: 3
-{tags-primary}`Fundamentals` \
-{tags-secondary}`Time Series` \
-{tags-secondary}`Anomaly Detection` \
-{tags-secondary}`Prediction / Forecasting`
+Use MLflow with CrateDB for experiment tracking and model registry.
+:::{seealso}
+Please navigate to the dedicated page about {ref}`mlflow`.
 :::
-::::
 
 
 ### PyCaret
@@ -284,14 +221,6 @@ solution.
 [Machine Learning and CrateDB: An introduction]: https://cratedb.com/blog/machine-learning-and-cratedb-part-one
 [Machine Learning and CrateDB: Getting Started With Jupyter]: https://cratedb.com/blog/machine-learning-cratedb-jupyter
 [Machine Learning and CrateDB: Experiment Design & Linear Regression]: https://cratedb.com/blog/machine-learning-and-cratedb-part-three-experiment-design-and-linear-regression
-[MLflow]: https://mlflow.org/
-[MLflow adapter for CrateDB]: https://github.com/crate/mlflow-cratedb
-[MLflow and CrateDB]: https://github.com/crate/cratedb-examples/tree/main/topic/machine-learning/mlflow
-[mlflow-cratedb]: https://pypi.org/project/mlflow-cratedb/
-[MLflow Tracking]: https://mlflow.org/docs/latest/tracking.html
 [MLOps]: https://en.wikipedia.org/wiki/MLOps
 [pandas]: https://pandas.pydata.org/
 [scikit-learn]: https://scikit-learn.org/
-[Time Series Modeling using Machine Learning]: https://cratedb.com/blog/introduction-to-time-series-modeling-with-cratedb-machine-learning-time-series-data
-[tracking-merlion-colab]: https://colab.research.google.com/github/crate/cratedb-examples/blob/main/topic/machine-learning/mlflow/tracking_merlion.ipynb
-[tracking-merlion-github]: https://github.com/crate/cratedb-examples/blob/main/topic/machine-learning/mlflow/tracking_merlion.ipynb