Update docs links; Remove ModelSchema usage (#316) (#320)

Author: Maxxx-zh

batch-ai-systems/fraud_batch/1_fraud_batch_feature_pipeline.ipynb

@@ -363,7 +363,7 @@
    "source": [
     "### <span style=\"color:#ff5f27;\"> 🪄 Creating Feature Groups </span>\n",
     "\n",
-    "A [feature group](https://docs.hopsworks.ai/3.0/concepts/fs/feature_group/fg_overview/) can be seen as a collection of conceptually related features. In this case, you will create a feature group for the transaction data and a feature group for the windowed aggregations on the transaction data. Both will have `cc_num` as primary key, which will allow you to join them when creating a dataset in the next tutorial.\n",
+    "A [feature group](https://docs.hopsworks.ai/latest/concepts/fs/feature_group/fg_overview/) can be seen as a collection of conceptually related features. In this case, you will create a feature group for the transaction data and a feature group for the windowed aggregations on the transaction data. Both will have `cc_num` as primary key, which will allow you to join them when creating a dataset in the next tutorial.\n",
     "\n",
     "Feature groups can also be used to define a namespace for features. For instance, in a real-life setting you would likely want to experiment with different window lengths. In that case, you can create feature groups with identical schema for each window length. \n",
     "\n",

integrations/big_query/big_query_feature_pipeline_external.ipynb

@@ -7,9 +7,9 @@
    "source": [
     "# <span style='color:#ff5f27'> 👨🏻‍🏫 BigQuery External Feature Group Creation</span>\n",
     "\n",
-    "Follow this [guide](https://docs.hopsworks.ai/3.0/user_guides/fs/storage_connector/creation/bigquery/) to set up a connection to BigQuery.\n",
+    "Follow this [guide](https://docs.hopsworks.ai/latest/user_guides/fs/storage_connector/creation/bigquery/) to set up a connection to BigQuery.\n",
     "\n",
-    "In addition, you can read about [External Feature Groups](https://docs.hopsworks.ai/3.0/user_guides/fs/feature_group/create_external/)."
+    "In addition, you can read about [External Feature Groups](https://docs.hopsworks.ai/latest/user_guides/fs/feature_group/create_external/)."
    ]
   },
   {

integrations/big_query/big_query_feature_pipeline_online_external.ipynb

@@ -7,9 +7,9 @@
    "source": [
     "# <span style='color:#ff5f27'> 👨🏻‍🏫 BigQuery Online External Feature Group Creation</span>\n",
     "\n",
-    "Follow this [guide](https://docs.hopsworks.ai/3.0/user_guides/fs/storage_connector/creation/bigquery/) to set up a connection to BigQuery.\n",
+    "Follow this [guide](https://docs.hopsworks.ai/latest/user_guides/fs/storage_connector/creation/bigquery/) to set up a connection to BigQuery.\n",
     "\n",
-    "In addition, you can read about [External Feature Groups](https://docs.hopsworks.ai/3.0/user_guides/fs/feature_group/create_external/)."
+    "In addition, you can read about [External Feature Groups](https://docs.hopsworks.ai/latest/user_guides/fs/feature_group/create_external/)."
    ]
   },
   {

integrations/bytewax/README.md

@@ -36,7 +36,7 @@ Currently, bytewax support for Hopsworks feature store is experimental and only
 that Feature group metadata needs to be registered in Hopsworks Feature store before you can write real time features computed
 by Bytewax.
 
-Full documentation how to create feature group using HSFS APIs can be found [here](https://docs.hopsworks.ai/3.4/user_guides/fs/feature_group/create/).
+Full documentation how to create feature group using HSFS APIs can be found [here](https://docs.hopsworks.ai/latest/user_guides/fs/feature_group/create/).
 
 This tutorial comes with a python program to create a feature group:
 - `python ./setup/feature_group.py`

integrations/federated-offline-query/federated-offline-query.ipynb

@@ -12,7 +12,7 @@
     "The aim of this tutorial is to create a unified view of features regarding the 100 most popular GitHub projects joining public datasets on Snowflake ([GitHub Archive](https://app.snowflake.com/marketplace/listing/GZTSZAS2KJ3/cybersyn-inc-github-archive?search=software&categorySecondary=%5B%2213%22%5D)). BigQuery ([deps.dev](https://console.cloud.google.com/marketplace/product/bigquery-public-data/deps-dev?hl=en)) and Hopsworks. We will create feature groups for each of these sources and then combine them in a unified view exposing all features together regardless of their source. We then use the view to create training data for a model predicting the code coverage of Github projects.\n",
     "\n",
     "## Prerequisites:\n",
-    "* To follow this tutorial you can sign up for the [Hopsworks Free Tier](https://app.hopsworks.ai/) or use  your own Hopsworks installation. You also need access to Snowflake and BigQuery, which offer free trials: [Snowflake Free Trial](https://signup.snowflake.com/?utm_source=google&utm_medium=paidsearch&utm_campaign=em-se-en-brand-trial-exact&utm_content=go-rsa-evg-ss-free-trial&utm_term=c-g-snowflake%20trial-e&_bt=591349674928&_bk=snowflake%20trial&_bm=e&_bn=g&_bg=129534995484&gclsrc=aw.ds&gad_source=1&gclid=EAIaIQobChMI0eeI-rPrggMVOQuiAx3WfgzdEAAYASAAEgIwS_D_BwE), [Google Cloud Free Tier](https://cloud.google.com/free?hl=en). If you choose to use your own Hopsworks, you should have an instance of Hopsworks version 3.5 or above and be the Data Owner/Author of a project. Furthermore, to use the  Hopsworks Feature Query Service, the user has to configure the Hopsworks cluster to enable it. This can only be done during [cluster creation](https://docs.hopsworks.ai/3.5/setup_installation/common/arrow_flight_duckdb/)."
+    "* To follow this tutorial you can sign up for the [Hopsworks Free Tier](https://app.hopsworks.ai/) or use  your own Hopsworks installation. You also need access to Snowflake and BigQuery, which offer free trials: [Snowflake Free Trial](https://signup.snowflake.com/?utm_source=google&utm_medium=paidsearch&utm_campaign=em-se-en-brand-trial-exact&utm_content=go-rsa-evg-ss-free-trial&utm_term=c-g-snowflake%20trial-e&_bt=591349674928&_bk=snowflake%20trial&_bm=e&_bn=g&_bg=129534995484&gclsrc=aw.ds&gad_source=1&gclid=EAIaIQobChMI0eeI-rPrggMVOQuiAx3WfgzdEAAYASAAEgIwS_D_BwE), [Google Cloud Free Tier](https://cloud.google.com/free?hl=en). If you choose to use your own Hopsworks, you should have an instance of Hopsworks version 3.5 or above and be the Data Owner/Author of a project. Furthermore, to use the  Hopsworks Feature Query Service, the user has to configure the Hopsworks cluster to enable it. This can only be done during [cluster creation](https://docs.hopsworks.ai/latest/setup_installation/common/arrow_flight_duckdb/)."
    ]
   },
   {
@@ -33,7 +33,7 @@
    "source": [
     "## <span style='color:#ff5f27'> Set up the Snowflake and BigQuery in Hopsworks\n",
     "\n",
-    "Hopsworks manages the connection to Snowflake and BigQuery through storage connectors. Follow the [Storage Connector Guides](https://docs.hopsworks.ai/3.5/user_guides/fs/storage_connector/) to configure storage connectors for Snowflake and BigQuery and name them **Snowflake** and **BigQuery**."
+    "Hopsworks manages the connection to Snowflake and BigQuery through storage connectors. Follow the [Storage Connector Guides](https://docs.hopsworks.ai/latest/user_guides/fs/storage_connector/) to configure storage connectors for Snowflake and BigQuery and name them **Snowflake** and **BigQuery**."
    ]
   },
   {

integrations/gcs/gcs_feature_pipeline_external.ipynb

@@ -7,9 +7,9 @@
    "source": [
     "# <span style='color:#ff5f27'> 👨🏻‍🏫 GCS External Feature Group Creation</span>\n",
     "\n",
-    "Follow this [guide](https://docs.hopsworks.ai/3.1/user_guides/fs/storage_connector/creation/gcs/) to set up a connection to GCS.\n",
+    "Follow this [guide](https://docs.hopsworks.ai/latest/user_guides/fs/storage_connector/creation/gcs/) to set up a connection to GCS.\n",
     "\n",
-    "In addition, you can read about [External Feature Groups](https://docs.hopsworks.ai/3.0/user_guides/fs/feature_group/create_external/)."
+    "In addition, you can read about [External Feature Groups](https://docs.hopsworks.ai/latest/user_guides/fs/feature_group/create_external/)."
    ]
   },
   {

integrations/gcs/gcs_feature_pipeline_online_external.ipynb

@@ -7,9 +7,9 @@
    "source": [
     "# <span style='color:#ff5f27'> 👨🏻‍🏫 GCS Online External Feature Group Creation</span>\n",
     "\n",
-    "Follow this [guide](https://docs.hopsworks.ai/3.1/user_guides/fs/storage_connector/creation/gcs/) to set up a connection to GCS.\n",
+    "Follow this [guide](https://docs.hopsworks.ai/latest/user_guides/fs/storage_connector/creation/gcs/) to set up a connection to GCS.\n",
     "\n",
-    "In addition, you can read about [External Feature Groups](https://docs.hopsworks.ai/3.0/user_guides/fs/feature_group/create_external/)."
+    "In addition, you can read about [External Feature Groups](https://docs.hopsworks.ai/latest/user_guides/fs/feature_group/create_external/)."
    ]
   },
   {

integrations/java/beam/README.md

@@ -32,7 +32,7 @@ Currently, Beam support for Hopsworks feature store is experimental and only wri
 that Feature group metadata needs to be registered in Hopsworks Feature store before you can write real time features computed
 by Bytewax.
 
-Full documentation how to create feature group using HSFS APIs can be found [here](https://docs.hopsworks.ai/3.4/user_guides/fs/feature_group/create/).
+Full documentation how to create feature group using HSFS APIs can be found [here](https://docs.hopsworks.ai/latest/user_guides/fs/feature_group/create/).
 
 This tutorial comes with a python program to create a feature group:
 - `python ./setup/feature_group.py`
@@ -46,7 +46,7 @@ service account has the Pub/Sub Admin role.
 
 ### Google Cloud Pub/Sub to Google Cloud Storage
 Now you ready to run a streaming pipeline using Beam and Google Cloud Dataflow. For this you need to
-have Hopsworks cluster host address, hopsworks project name and [api key](https://docs.hopsworks.ai/3.3/user_guides/projects/api_key/create_api_key/)
+have Hopsworks cluster host address, hopsworks project name and [api key](https://docs.hopsworks.ai/latest/user_guides/projects/api_key/create_api_key/)
 
 Once you have the above define environment variables:
 

integrations/java/java/README.md

@@ -15,7 +15,7 @@ mvn clean package
 
 ## Execute java application:
 Now you will create [connection](https://docs.hopsworks.ai/hopsworks-api/3.3/generated/api/connection/) with
-your Hopsworks cluster. For this you need to have Hopsworks cluster host address and [api key](https://docs.hopsworks.ai/3.3/user_guides/projects/api_key/create_api_key/)
+your Hopsworks cluster. For this you need to have Hopsworks cluster host address and [api key](https://docs.hopsworks.ai/latest/user_guides/projects/api_key/create_api_key/)
 
 Then define environment variables 
 

integrations/neo4j/2_training_pipeline.ipynb

@@ -132,7 +132,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "###### <span style=\"color:#ff5f27;\"> 🤖 Transformation Functions </span>\n",
+    "### <span style=\"color:#ff5f27;\"> 🤖 Transformation Functions </span>\n",
     "\n",
     "Transformation functions are a mathematical mapping of input data that may be stateful - requiring statistics from the partent feature view (such as number of instances of a category, or mean value of a numerical feature)\n",
     "\n",
@@ -438,42 +438,6 @@
     "}"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### <span style=\"color:#ff5f27;\">⚙️ Model Schema</span>\n",
-    "\n",
-    "The model needs to be set up with a [Model Schema](https://docs.hopsworks.ai/3.0/user_guides/mlops/registry/model_schema/), which describes the inputs and outputs for a model.\n",
-    "\n",
-    "A Model Schema can be automatically generated from training examples, as shown below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from hsml.schema import Schema\n",
-    "from hsml.model_schema import ModelSchema\n",
-    "\n",
-    "# Define the input schema using the values of X_train\n",
-    "input_schema = Schema(X_train)\n",
-    "\n",
-    "# Define the output schema using y_train\n",
-    "output_schema = Schema(y_train)\n",
-    "\n",
-    "# Create a ModelSchema object specifying the input and output schemas\n",
-    "model_schema = ModelSchema(\n",
-    "    input_schema=input_schema, \n",
-    "    output_schema=output_schema,\n",
-    ")\n",
-    "\n",
-    "# Convert the model schema to a dictionary for further inspection or serialization\n",
-    "model_schema.to_dict()"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -511,9 +475,9 @@
     "mr_model = mr.tensorflow.create_model(\n",
     "    name=\"aml_model\",                                    # Specify the model name\n",
     "    metrics=metrics,                                     # Include model metrics\n",
-    "    model_schema=model_schema,                           # Include model schema\n",
     "    description=\"Adversarial anomaly detection model.\",  # Model description\n",
     "    input_example=[\"70408aef\"],                          # Input example\n",
+    "    feature_view=feature_view,\n",
     ")\n",
     "\n",
     "# Save the registered model to the model registry\n",