[docs/ci] Fix (some) broken linkchecks (#28087)

Signed-off-by: Kai Fricke <kai@anyscale.com>

doc/source/data/examples/big_data_ingestion.ipynb

@@ -362,7 +362,7 @@
     "# -> throughput: 8.56GiB/s\n",
     "```\n",
     "\n",
-    "Note: The pipeline can also be submitted using [Ray Job Submission](https://docs.ray.io/en/latest/cluster/job-submission.html) ,\n",
+    "Note: The pipeline can also be submitted using [Ray Job Submission](https://docs.ray.io/en/latest/cluster/running-applications/job-submission/) ,\n",
     "which is in beta starting with Ray 1.12.  Try it out!"
    ]
   }

doc/source/ray-air/examples/huggingface_text_classification.ipynb

@@ -61,7 +61,7 @@
       "source": [
         "We will use `ray.init()` to initialize a local cluster. By default, this cluster will be compromised of only the machine you are running this notebook on. You can also run this notebook on an Anyscale cluster.\n",
         "\n",
-        "This notebook *will not* run in [Ray Client](https://docs.ray.io/en/latest/cluster/ray-client.html) mode."
+        "This notebook *will not* run in [Ray Client](https://docs.ray.io/en/latest/cluster/running-applications/job-submission/ray-client.html) mode."
       ]
     },
     {

doc/source/ray-air/examples/tfx_tabular_train_to_serve.ipynb

@@ -784,7 +784,7 @@
     "id": "OlzjlW8QR_q6"
    },
    "source": [
-    "We will use Ray Serve to serve the trained model. A core concept of Ray Serve is [Deployment](https://docs.ray.io/en/latest/serve/core-apis.html). It allows you to define and update your business logic or models that will handle incoming requests as well as how this is exposed over HTTP or in Python.\n",
+    "We will use Ray Serve to serve the trained model. A core concept of Ray Serve is [Deployment](https://docs.ray.io/en/latest/serve/getting_started.html#converting-to-a-ray-serve-deployment). It allows you to define and update your business logic or models that will handle incoming requests as well as how this is exposed over HTTP or in Python.\n",
     "\n",
     "In the case of serving model, `ray.serve.air_integrations.Predictor` and `ray.serve.air_integrations.PredictorDeployment` wrap a `ray.air.checkpoint.Checkpoint` into a Ray Serve deployment that can readily serve HTTP requests.\n",
     "Note, ``Checkpoint`` captures both model and preprocessing steps in a way compatible with Ray Serve and ensures that ml workload can transition seamlessly between training and\n",

python/ray/client_builder.py

@@ -22,7 +22,10 @@ from ray.widgets import Template
 
 logger = logging.getLogger(__name__)
 
-CLIENT_DOCS_URL = "https://docs.ray.io/en/latest/cluster/ray-client.html"
+CLIENT_DOCS_URL = (
+    "https://docs.ray.io/en/latest/cluster/running-applications/"
+    "job-submission/ray-client.html"
+)
 
 
 @dataclass