standardize sagemaker doc (#821)

* Update Sagemaker docs

* make fixes

---------

Co-authored-by: HarshCasper <erbeusgriffincasper@gmail.com>

content/en/user-guide/aws/sagemaker/index.md

@@ -1,61 +1,104 @@
 ---
 title: "SageMaker"
 linkTitle: "SageMaker"
-categories: ["LocalStack Pro"]
-description: >
-  Get started with AWS SageMaker on LocalStack
-aliases:
-  - /aws/sagemaker/
+description: Get started with SageMaker on LocalStack
 ---
 
-LocalStack Pro provides a local version of the SageMaker API, which allows running jobs to create machine learning models (e.g., using PyTorch) and to deploy them.
+## Introduction 
 
-## Model Training
+Amazon SageMaker is a fully managed service provided by Amazon Web Services (AWS) that provides the tools to build, train, and deploy machine-learning models in the cloud for predictive analytics applications. It streamlines the machine learning development process, reduces the time and effort required to build and deploy models, and offers the scalability and flexibility needed for large-scale machine learning projects in the AWS cloud.
 
-A basic training example using the `sagemaker.tensorflow.TensorFlow` class is provided in [this Github repository](https://github.com/localstack/localstack-pro-samples/tree/master/sample-archive/sagemaker-ml-jobs). Essentially, the code boils down to these core lines:
-```python3
-inputs = ...  # load training data files
-mnist_estimator = TensorFlow(entry_point='mnist.py', role='arn:aws:...',
-    framework_version='1.12.0', sagemaker_session=sagemaker_session,
-    train_instance_count=1, training_steps=10, evaluation_steps=10)
-mnist_estimator.fit(inputs, logs=False)
-```
+LocalStack Pro provides a local version of the SageMaker API, which allows running jobs to create machine learning models (e.g., using PyTorch) and to deploy them. The supported APIs are available on our [API coverage page](https://docs.localstack.cloud/references/coverage/coverage_sagemaker/), which provides information on the extent of Sagemaker's integration with LocalStack.
+
+## Getting started
+
+This guide is designed for users new to SageMaker and assumes basic knowledge of Python3 and [AWS SDK for Python (Boto3)](https://aws.amazon.com/sdk-for-python/).
+
+We will demonstrate an application illustrating running a machine learning job using the SageMaker API locally that perform the following:
+
+- Set up an MNIST model in SageMaker using LocalStack.
+- Creates a SageMaker Endpoint for accessing the model
+- Invokes the endpoint directly on the container via Boto3
 
-The code snippet above uploads the model training code to local S3, submits a new training job to the local SageMaker API, and finally puts the trained model back to an output S3 bucket. Please refer to the sample repo for more details.
+{{< alert title="Note" >}}
+SageMaker is a fairly comprehensive API for now. Currently a subset of the functionality is provided locally, but new features are being added on a regular basis.
+{{< /alert >}}
 
-## Model Deployment and Inference
+### Download the sample application
 
-SageMaker supports the deployment and real-time inference of singular local ML models. An example for that is provided in our [PRO samples repository](https://github.com/localstack/localstack-pro-samples/tree/master/sagemaker-inference). As explained in the ReadMe of the sample, you will need to retrieve the image with your AWS account by connecting with [the provided ECR repository](https://github.com/aws/deep-learning-containers/blob/master/available_images.md):
+You can download the sample application from [GitHub](https://github.com/localstack/localstack-pro-samples/tree/master/sagemaker-inference) or by running the following commands:
+
+{{< command >}}
+$ mkdir localstack-samples && cd localstack-samples
+$ git init
+$ git remote add origin -f git@github.com:localstack/localstack-pro-samples.git
+$ git config core.sparseCheckout true
+$ echo sagemaker-inference >> .git/info/sparse-checkout
+$ git pull origin master
+{{< /command >}}
+
+### Set up the environment
+
+After downloading the sample application, you can set up your Docker Client to pull the AWS Deep Learning images by running the following command:
 
 {{< command >}}
 $ aws ecr get-login-password --region us-east-1 | docker login --username AWS --password-stdin 763104351884.dkr.ecr.us-east-1.amazonaws.com
 {{< /command >}}
 
-The example also shows the two currently supported options of inference - on the container itself or through the `sagemaker-runtime` invocation endpoint:
-
-```python
-def inference_model_container(run_id: str = "0"):
-    ep = sagemaker.describe_endpoint(EndpointName=f"{ENDPOINT_NAME}-{run_id}")
-    arn = ep["EndpointArn"]
-    tag_list = sagemaker.list_tags(ResourceArn=arn)
-    port = "4510"
-    for tag in tag_list["Tags"]:
-        if tag["Key"] == "_LS_ENDPOINT_PORT_":
-            port = tag["Value"]
-    inputs = _get_input_dict()
-    response = httpx.post(f"http://localhost.localstack.cloud:{port}/invocations", json=inputs,
-                          headers={"Content-Type": "application/json", "Accept": "application/json"})
-    _show_predictions(json.loads(response.text))
-
-
-def inference_model_boto3(run_id: str = "0"):
-    inputs = _get_input_dict()
-    response = sagemaker_runtime.invoke_endpoint(EndpointName=f"{ENDPOINT_NAME}-{run_id}", Body=json.dumps(inputs),
-                                                 Accept="application/json",
-                                                 ContentType="application/json")
-    _show_predictions(json.loads(response["Body"].read()))
+Since the images are quite large (several gigabytes), it's a good idea to pull the images using Docker in advance.
+
+{{< command >}}
+$ docker pull 763104351884.dkr.ecr.us-east-1.amazonaws.com/pytorch-inference:1.5.0-cpu-py3
+{{< /command >}}
+
+### Run the sample application
+
+Start your LocalStack container using your preferred method. Run the sample application by executing the following command:
+
+{{< command >}}
+$ python3 main.,py
+{{< /command >}}
+
+You should see the following output:
+
+```bash
+Creating bucket...
+Uploading model data to bucket...
+Creating model in SageMaker...
+Adding endpoint configuration...
+Creating endpoint...
+Checking endpoint status...
+Endpoint not ready - waiting...
+Checking endpoint status...
+Endpoint ready!
+Invoking via boto...
+Predicted digits: [7, 3]
+Invoking endpoint directly...
+Predicted digits: [2, 6]
 ```
 
-{{< alert title="Note" >}}
-SageMaker is a fairly comprehensive API - for now, only a subset of the functionality is provided locally, but new features are being added on a regular basis.
-{{< /alert >}}
+You can also invoke a serverless endpoint, by navigating to `main.py` and uncommenting the [`run_serverless`](https://github.com/localstack/localstack-pro-samples/blob/cca7a59e0b2b46a18a3db226c31d44401b68447e/sagemaker-inference/main.py#L134) function call.
+
+## Resource Browser
+
+The LocalStack Web Application provides a [Resource Browser](https://docs.localstack.cloud/user-guide/web-application/resource-browser/) for managing Lambda resources. You can access the Resource Browser by opening the LocalStack Web Application in your browser, navigating to the **Resources** section, and then clicking on **Sagemaker** under the **Compute** section.
+
+The Resource Browser displays Models, Endpoint Configurations and Endpoint. You can click on individual resources to view their details.
+
+<img src="sagemaker-resource-browser.png" alt="Sagemaker Resource Browser" title="Lambda Resource Browser" width="900" />
+
+The Resource Browser allows you to perform the following actions:
+
+- **Create and Remove Models**: You can remove existing model and create a new model with the required configuration
+
+
+  <img src="sagemaker-create-model.png" alt="Sagemaker Resource Browser" title="Lambda Resource Browser" width="900" />
+
+
+- **Endpoint Configurations & Endpoints**: You can create endpoints from the resource browser that hosts your deployed machine learning model. You can also create endpoint configuration that specifies the type and number of instances that will be used to serve your model on an endpoint.
+
+## Examples 
+
+The following code snippets and sample applications provide practical examples of how to use Sagemaker in LocalStack for various use cases:
+
+- [MNIST handwritten digit recognition model](https://github.com/localstack-samples/sample-mnist-digit-recognition-sagemaker) demonstrates a web application that allows users to draw a digit and submit it to a locally running SageMaker endpoint.