updating docs

Author: shivanissambare

articles/machine-learning/concept-prebuilt-docker-images-inference.md

@@ -17,8 +17,6 @@ ms.custom: deploy, docker, prebuilt
 
 Prebuilt Docker container images for inference are used when deploying a model with Azure Machine Learning.  The images are prebuilt with popular machine learning frameworks and Python packages. You can also extend the packages to add other packages by using one of the following methods:
 
-* [Use prebuilt inference image as base for a new Dockerfile](how-to-extend-prebuilt-docker-image-inference.md). Using this method, you can install both **Python packages and apt packages**.
-
 ## Why should I use prebuilt images?
 
 * Reduces model deployment latency.
@@ -29,11 +27,16 @@ Prebuilt Docker container images for inference are used when deploying a model w
 ## List of prebuilt Docker images for inference 
 
 > [!IMPORTANT]
-> The list provided below includes only **currently supported** inference images by Azure Machine Learning.
+> The list provided below includes only **currently supported** inference docker images by Azure Machine Learning.
 
 [!INCLUDE [list-of-inference-prebuilt-docker-images](../../includes/aml-inference-list-prebuilt-docker-images.md)]
 
+## How to use inference prebuilt docker images?
+
+[Check examples in the Azure machine learning GitHub repository](https://github.com/Azure/azureml-examples/tree/main/cli/endpoints/online/custom-container)
+
 ## Next steps
 
-* [Add Python packages to prebuilt images](how-to-prebuilt-docker-images-inference-python-extensibility.md).
-* [Use a prebuilt package as a base for a new Dockerfile](how-to-extend-prebuilt-docker-image-inference.md).
+* [Deploy and score a machine learning model by using an online endpoint](how-to-deploy-managed-online-endpoints.md)
+* [Learn more about custom containers](how-to-deploy-custom-container.md)
+* [azureml-examples GitHub repository](https://github.com/Azure/azureml-examples/tree/main/cli/endpoints/online)

articles/machine-learning/how-to-inference-server-http.md

@@ -175,7 +175,7 @@ You have **Flask 2** installed in your python environment but are running a serv
 ImportError: cannot import name 'Markup' from 'jinja2'
 ```
 
-Some older versions of the server do not have the correct boundaries for these dependencies. Please upgrade to the latest version of the server. We also recommend to not include these packages in their dependencies and rely on our server package to install the compatible versions of these dependencies.
+Older versions (<= 0.4.10) of the server did not pin Flask's dependency to compatible versions. This is fixed in the latest version of the server.
 
 ### 3. Do I need to reload the server when changing the score script?
 
@@ -187,5 +187,5 @@ The Azure Machine Learning inference server runs on Windows & Linux based operat
 
 ## Next steps
 
-* For more information on creating an entry script and deploying models, see [How to deploy a model using Azure Machine Learning](how-to-deploy-and-where.md).
+* For more information on creating an entry script and deploying models, see [How to deploy a model using Azure Machine Learning](how-to-deploy-managed-online-endpoints.md).
 * Learn about [Prebuilt docker images for inference](concept-prebuilt-docker-images-inference.md)

includes/aml-inference-list-prebuilt-docker-images.md

@@ -12,45 +12,12 @@ ms.date: 07/14/2022
 
 * All the docker images run as non-root user.
 * We recommend using `latest` tag for docker images. Prebuilt docker images for inference are published to Microsoft container registry (MCR), to query list of tags available, follow [instructions on the GitHub repository](https://github.com/microsoft/ContainerRegistry#browsing-mcr-content).
+* If you want to use a specific tag for any inference docker image, we support from `latest` to the tag that is *6 months* old from the `latest`.  
 
-### No framework
+### Inference minimal base images
 
 Framework version | CPU/GPU | Pre-installed packages | MCR Path
  --- | --- | --- | --- |
 NA | CPU | NA | `mcr.microsoft.com/azureml/minimal-ubuntu18.04-py37-cpu-inference:latest`
 NA | GPU | NA | `mcr.microsoft.com/azureml/minimal-ubuntu18.04-py37-cuda11.0.3-gpu-inference:latest`
 NA | CPU | NA | `mcr.microsoft.com/azureml/minimal-ubuntu20.04-py38-cpu-inference:latest`
-
-### TensorFlow
-
-Framework version | CPU/GPU | Pre-installed packages | MCR Path 
- --- | --- | --- | --- |
-2.4 | CPU | numpy>=1.16.0 </br> pandas~=1.1.x | `mcr.microsoft.com/azureml/tensorflow-2.4-ubuntu18.04-py37-cpu-inference:latest` 
-2.4 | GPU | numpy >= 1.16.0 </br> pandas~=1.1.x </br> CUDA==11.0.3 </br> CuDNN==8.0.5.39 | `mcr.microsoft.com/azureml/tensorflow-2.4-ubuntu18.04-py37-cuda11.0.3-gpu-inference:latest`
-1.15 | CPU | pandas == 0.25.1 </br> numpy == 1.20.1 | `mcr.microsoft.com/azureml/tensorflow-1.15-ubuntu18.04-py37-cpu-inference:latest` 
-
-### PyTorch
-
-Framework version | CPU/GPU | Pre-installed packages | MCR Path
- --- | --- | --- | --- |
-1.10 | CPU | numpy >= 1.16.0 </br> pandas >= 1.1, < 1.2 | `mcr.microsoft.com/azureml/pytorch-1.10-ubuntu18.04-py37-cpu-inference` 
-1.9 | CPU | numpy >= 1.16.0 </br> pandas >= 1.1, < 1.2 | `mcr.microsoft.com/azureml/pytorch-1.9-ubuntu18.04-py37-cpu-inference:latest` 
-1.9 | GPU | | `mcr.microsoft.com/azureml/pytorch-1.9-ubuntu18.04-py37-cuda11.0.3-gpu-inference:latest` 
-
-### SciKit-Learn
-
-Framework version | CPU/GPU | Pre-installed packages | MCR Path
- --- | --- | --- | --- | 
-0.24.1  | CPU | scikit-learn==0.24.1 </br> numpy>=1.16.0 </br> pandas~=1.1.x | `mcr.microsoft.com/azureml/sklearn-0.24.1-ubuntu18.04-py37-cpu-inference:latest`
-
-### LightGBM
-
-Framework version | CPU/GPU | Pre-installed packages | MCR Path
- --- | --- | --- | --- |
-3.2 | CPU | scikit-learn==0.24.1 </br> numpy>=1.16.0, <1.20 </br> pandas>=1.1, <1.2 | `mcr.microsoft.com/azureml/lightgbm-3.2-ubuntu18.04-py37-cpu-inference:latest`
-
-### XGBoost
-
-Framework version | CPU/GPU | Pre-installed packages | MCR Path
- --- | --- | --- | --- |
-0.9 | CPU | scikit-learn==0.23.2 </br> numpy==1.20.1 </br> pandas==0.25.1 | `mcr.microsoft.com/azureml/xgboost-0.9-ubuntu18.04-py37-cpu-inference:latest` |