Update data loading docs

docs/docs.json

@@ -67,7 +67,7 @@
             "group": "Model training",
             "pages": [
               "training/why-lancedb",
-              "training/index",
+              "training/data-loading",
               "training/torch",
               "training/object-detection",
               "training/vlm-finetuning",
@@ -573,6 +573,10 @@
     {
       "source": "/huggingface/datasets",
       "destination": "/datasets"
+    },
+    {
+      "source": "/training",
+      "destination": "/training/data-loading"
     }
   ]
 }

docs/index.mdx

@@ -61,7 +61,7 @@ feature engineering, search and retrieval, and efficient training data access.
   <Card title="Train and fine-tune models" icon="fire" href="/training/why-lancedb">
     Learn why LanceDB works well as the data layer for training workloads.
   </Card>
-  <Card title="Load data into PyTorch" icon="boxes-stacked" href="/training/">
+  <Card title="Load data into PyTorch" icon="boxes-stacked" href="/training/data-loading">
     Use LanceDB tables and permutations for projected, shuffled, random-access training reads.
   </Card>
   <Card title="Browse ready-to-use datasets" icon="database" href="/datasets">

docs/quickstart.mdx

@@ -466,7 +466,7 @@ updates, versioning, indexing, full-text search, hybrid search, and reranking.
 <Card
   title="Data loading and shuffles"
   icon="boxes-stacked"
-  href="/training/"
+  href="/training/data-loading"
 >
   Use LanceDB for projected, shuffled, random-access reads in training workflows.
 </Card>

docs/training/object-detection.mdx

@@ -7,7 +7,7 @@ icon: car
 
 This example walks through fine-tuning an autonomous vehicle (AV) perception model on targeted failure-mode slices of [BDD100K](https://www.bdd100k.com/) — riders, nighttime pedestrians, and distant pedestrians — using LanceDB as a single multimodal table from raw JPEG bytes through to the PyTorch training loop.
 
-The full pipeline lives in the [lancedb/training](https://github.com/lancedb/training/tree/main/object-detection) repository. This page focuses on the parts most relevant to training: defining curated splits as materialized views, loading them through the [`Permutation`](/training/) API, and pinning checkpoints to an exact data version.
+The full pipeline lives in the [lancedb/training](https://github.com/lancedb/training/tree/main/object-detection) repository. This page focuses on the parts most relevant to training: defining curated splits as materialized views, loading them through the [`Permutation`](/training/data-loading) API, and pinning checkpoints to an exact data version.
 
 ## What you get
 
@@ -155,7 +155,7 @@ with gconn.local_ray_context():
 
 ## 4. PyTorch DataLoader via the Permutation API
 
-The training script doesn't know about the filter — it opens a view by name and reads through the [`Permutation`](/training/) API. Each DataLoader worker reopens its own connection lazily, reads Arrow batches directly from Lance (zero-copy, no intermediate file format), and the collate function decodes the whole batch in one pass. `Permutation` provides random-access indexing over the table, so shuffling is a cheap pointer rewrite rather than a full-dataset shuffle on disk.
+The training script doesn't know about the filter — it opens a view by name and reads through the [`Permutation`](/training/data-loading) API. Each DataLoader worker reopens its own connection lazily, reads Arrow batches directly from Lance (zero-copy, no intermediate file format), and the collate function decodes the whole batch in one pass. `Permutation` provides random-access indexing over the table, so shuffling is a cheap pointer rewrite rather than a full-dataset shuffle on disk.
 
 ```py Python icon=Python
 import lancedb

docs/training/torch.mdx

@@ -32,7 +32,7 @@ for batch in dataloader:
 ```
 
 Although the `Table` class in LanceDB implements the `torch.utils.data.Dataset` interface, you may find that using
-a table [Permutation](/training/) is more flexible.
+a table [Permutation](/training/data-loading) is more flexible.
 
 ```py Python icon=Python 
 from lancedb.permutation import Permutation

docs/training/why-lancedb.mdx

@@ -103,7 +103,7 @@ access patterns in one format instead of scattering them across task-specific st
 ## Next steps
 
 <CardGroup cols={2}>
-  <Card title="Data loading and shuffles" icon="boxes-stacked" href="/training/">
+  <Card title="Data loading and shuffles" icon="boxes-stacked" href="/training/data-loading">
     Learn how to use LanceDB permutations to select rows, project columns, split datasets, and shuffle training reads.
   </Card>
   <Card title="PyTorch integration" icon="fire" href="/training/torch">