Migrate experiment tracking documentation (#2250)

Resolves #2249

docs/source/experiment_tracking.md

@@ -1,7 +1,7 @@
 # Experiment tracking in Kedro-Viz
 
 ```{important}
-Starting from version 8.0.0 of Kedro-Viz, Experiment Tracking is exclusively supported for users with kedro-datasets version 2.1.0 or higher.
+Starting from Kedro-Viz 11.0.0, native experiment tracking will be deprecated in Kedro and Kedro-Viz. To enable experiment tracking in Kedro with MLflow, follow the migrate to [`kedro-mlflow` documentation](./migrate_experiment_tracking.md).
 ```
 
 Experiment tracking is the process of saving all the metadata related to an experiment each time you run it. It enables you to compare different runs of a machine-learning model as part of the experimentation process.

docs/source/index.md

@@ -31,6 +31,7 @@ share_kedro_viz
 preview_datasets
 slice_a_pipeline
 experiment_tracking
+migrate_experiment_tracking
 ```
 
 ```{toctree}

docs/source/migrate_experiment_tracking.md

@@ -0,0 +1,139 @@
+# Migration from Kedro-Viz native experiment tracking to `kedro-mlflow`
+
+With the deprecation of Kedro-Viz experiment tracking from version 11.0.0, transitioning to [`kedro-mlflow`](https://kedro-mlflow.readthedocs.io/en/stable/) offers enhanced experiment tracking and artifact management. This guide outlines the steps to:
+
+1. **Remove deprecated Kedro-Viz experiment tracking configurations.**
+2. **Update existing dataset configurations in the catalog to use `kedro-mlflow`.**
+3. **Optionally, delete old experiment tracking data.**
+
+## Remove deprecated Kedro-Viz experiment tracking configurations
+
+### 1. Remove session store setup
+
+In `src/<project_name>/settings.py`, locate and remove the session store configuration:
+
+```python
+from kedro_viz.integrations.kedro.sqlite_store import SQLiteStore
+
+SESSION_STORE_CLASS = SQLiteStore
+SESSION_STORE_ARGS = {"path": str(Path(__file__).parents[2] / "data")}
+```
+
+### 2. Remove environment variables
+Remove any environment variables associated with collaborative tracking:
+
+```bash
+unset KEDRO_SQLITE_STORE_USERNAME
+```
+
+### 3. Delete SQLite database
+If a `session_store.db` SQLite database was created, you can delete it to clean up your project. This file is typically located in one of the following directories:
+
+- The project root 
+- The `.viz` folder within the project root
+- The `data` folder
+
+## Update dataset configurations in catalog to use `kedro-mlflow`
+
+Update the dataset configurations in your `catalog.yml` to transition to `kedro-mlflow`. Refer to the table below for the changes:
+
+### Dataset migration table
+
+| Kedro-Viz Dataset Type         | MLflow Dataset Type        | Update Instructions                                      |
+|---------------------------------|----------------------------|---------------------------------------------------------|
+| `tracking.MetricsDataset`      | `MlflowMetricDataset`      | Update type to [`MlflowMetricDataset`](https://kedro-mlflow.readthedocs.io/en/stable/source/31_API/kedro_mlflow.io.html#kedro_mlflow.io.metrics.mlflow_metric_dataset.MlflowMetricDataset).                  |
+| `tracking.JSONDataset`         | `MlflowArtifactDataset`    | Wrap within [`MlflowArtifactDataset`](https://kedro-mlflow.readthedocs.io/en/stable/source/31_API/kedro_mlflow.io.html#kedro_mlflow.io.artifacts.mlflow_artifact_dataset.MlflowArtifactDataset) as `json.JSONDataset`. |
+| `plotly.plotlyDataset`         | `MlflowArtifactDataset`    | Wrap within [`MlflowArtifactDataset`](https://kedro-mlflow.readthedocs.io/en/stable/source/31_API/kedro_mlflow.io.html#kedro_mlflow.io.artifacts.mlflow_artifact_dataset.MlflowArtifactDataset) as `plotly.HTMLDataset`. |
+| `plotly.JSONDataset`           | `MlflowArtifactDataset`    | Wrap within [`MlflowArtifactDataset`](https://kedro-mlflow.readthedocs.io/en/stable/source/31_API/kedro_mlflow.io.html#kedro_mlflow.io.artifacts.mlflow_artifact_dataset.MlflowArtifactDataset) as `plotly.HTMLDataset`. |
+| `matplotlib.MatplotlibWriter`  | `MlflowArtifactDataset`    | Wrap within [`MlflowArtifactDataset`](https://kedro-mlflow.readthedocs.io/en/stable/source/31_API/kedro_mlflow.io.html#kedro_mlflow.io.artifacts.mlflow_artifact_dataset.MlflowArtifactDataset).                   |
+
+### Metrics dataset
+For `tracking.MetricsDataset`, update its type to [`MlflowMetricDataset`](https://kedro-mlflow.readthedocs.io/en/stable/source/31_API/kedro_mlflow.io.html#kedro_mlflow.io.metrics.mlflow_metric_dataset.MlflowMetricDataset):
+
+Before:
+```yaml
+metrics:
+  type: tracking.MetricsDataset
+  filepath: data/09_tracking/metrics.json
+  versioned: true
+```
+
+After:
+```yaml
+metrics:
+  type: kedro_mlflow.io.metrics.MlflowMetricDataset
+```
+
+### JSON dataset
+For `tracking.JSONDataset`, wrap it within [`MlflowArtifactDataset`](https://kedro-mlflow.readthedocs.io/en/stable/source/31_API/kedro_mlflow.io.html#kedro_mlflow.io.artifacts.mlflow_artifact_dataset.MlflowArtifactDataset) and configure it as `json.JSONDataset`:
+
+Before:
+```yaml
+companies_columns:
+  type: tracking.JSONDataset
+  filepath: data/09_tracking/json_data.json
+  versioned: true
+```
+
+After:
+```yaml
+companies_columns:
+  type: kedro_mlflow.io.artifacts.MlflowArtifactDataset
+  dataset:
+    type: json.JSONDataset
+    filepath: data/02_intermediate/companies_columns.json
+```
+
+### Plotly dataset
+For `plotly.plotlyDataset` and `plotly.JSONDataset`, wrap it within [`MlflowArtifactDataset`](https://kedro-mlflow.readthedocs.io/en/stable/source/31_API/kedro_mlflow.io.html#kedro_mlflow.io.artifacts.mlflow_artifact_dataset.MlflowArtifactDataset) and configure it as `plotly.HTMLDataset` to render interactive plots in the MLflow UI:
+
+Before:
+```yaml
+plotly_json_data:
+  type: plotly.JSONDataset
+  filepath: data/09_tracking/plotly.json
+```
+
+After:
+```yaml
+plotly_json_data:
+  type: kedro_mlflow.io.artifacts.MlflowArtifactDataset
+  dataset:
+    type: plotly.HTMLDataset
+    filepath: data/08_reporting/plotly.html
+```
+
+### Matplotlib writer
+For `matplotlib.MatplotlibWriter`, wrap it within [`MlflowArtifactDataset`](https://kedro-mlflow.readthedocs.io/en/stable/source/31_API/kedro_mlflow.io.html#kedro_mlflow.io.artifacts.mlflow_artifact_dataset.MlflowArtifactDataset):
+
+Before:
+```yaml
+confusion_matrix:
+  type: matplotlib.MatplotlibWriter
+  filepath: data/09_tracking/confusion_matrix.png
+  versioned: true
+```
+
+After:
+```yaml
+confusion_matrix:
+  type: kedro_mlflow.io.artifacts.MlflowArtifactDataset
+  dataset:
+    type: matplotlib.MatplotlibWriter
+    filepath: data/08_reporting/confusion_matrix.png
+```
+
+## [Optional] Delete old tracking data
+
+Old experiment tracking data stored in `data/09_tracking/` is no longer needed. You can delete this directory to clean up your project:
+
+```bash 
+rm -rf data/09_tracking/
+```
+
+## Refer to the `kedro-mlflow` documentation for further setup
+
+After completing these steps, follow the below MLflow documentation to complete your experiment tracking setup with MLflow.
+
+- [The official Kedro + MLflow guide in the Kedro documentation](https://docs.kedro.org/en/stable/integrations/mlflow.html)
+- The documentation of [`kedro-mlflow`](https://docs.kedro.org/en/latest/integrations/mlflow.html) plugin