Small dependency and docs updates (#2982)

* bump feast's dependencies

* update prodigy docs

* update tensorflow's dataset materializer

* fix label studio broken docs

docs/book/component-guide/annotators/label-studio.md

@@ -61,7 +61,7 @@ zenml secret create label_studio_secrets --api_key="<your_label_studio_api_key>"
 Then register your annotator with ZenML:
 
 ```shell
-zenml annotator register label_studio --flavor label_studio --api_key="{{label_studio_secrets.api_key}}"
+zenml annotator register label_studio --flavor label_studio --authentication_secret="label_studio_secrets" --port=8080
 
 # for deployed instances of Label Studio, you can also pass in the URL as follows, for example:
 # zenml annotator register label_studio --flavor label_studio --authentication_secret="<LABEL_STUDIO_SECRET_NAME>" --instance_url="<your_label_studio_url>" --port=80

docs/book/component-guide/annotators/prodigy.md

@@ -91,7 +91,7 @@ example, you might run:
 zenml annotator dataset annotate your_dataset --command="textcat.manual news_topics ./news_headlines.jsonl --label Technology,Politics,Economy,Entertainment"
 ```
 
-This would launch the Prodigy interface for the `textcat.manual` recipe with the
+This would launch the Prodigy interface for [the `textcat.manual` recipe](https://prodi.gy/docs/recipes#textcat-manual) with the
 `news_topics` dataset and the labels `Technology`, `Politics`, `Economy`, and
 `Entertainment`. The data would be loaded from the `news_headlines.jsonl` file.
 

src/zenml/integrations/feast/__init__.py

@@ -31,7 +31,7 @@ class FeastIntegration(Integration):
 
     NAME = FEAST
     # click is added to keep the feast click version in sync with ZenML's click
-    REQUIREMENTS = ["feast<=0.37.1", "click>=8.0.1,<8.1.4"]
+    REQUIREMENTS = ["feast", "click>=8.0.1,<8.1.4"]
     REQUIREMENTS_IGNORED_ON_UNINSTALL = ["click", "pandas"]
 
     @classmethod

src/zenml/integrations/tensorflow/materializers/tf_dataset_materializer.py

@@ -48,7 +48,7 @@ def load(self, data_type: Type[Any]) -> Any:
         temp_dir = tempfile.mkdtemp()
         io_utils.copy_dir(self.uri, temp_dir)
         path = os.path.join(temp_dir, DEFAULT_FILENAME)
-        dataset = tf.data.experimental.load(path)
+        dataset = tf.data.Dataset.load(path)
         # Don't delete the temporary directory here as the dataset is lazily
         # loaded and needs to read it when the object gets used
         return dataset
@@ -62,7 +62,7 @@ def save(self, dataset: tf.data.Dataset) -> None:
         temp_dir = tempfile.TemporaryDirectory()
         path = os.path.join(temp_dir.name, DEFAULT_FILENAME)
         try:
-            tf.data.experimental.save(
+            tf.data.Dataset.save(
                 dataset, path, compression=None, shard_func=None
             )
             io_utils.copy_dir(temp_dir.name, self.uri)