diff --git a/README.md b/README.md
index 2add5721..33d704d1 100644
--- a/README.md
+++ b/README.md
@@ -13,7 +13,7 @@ Read more detailed instructions on [how to set up your environment](./NBSETUP.md
 
 ## How to navigate and use the example notebooks?
 If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, you should always run the [Configuration](./configuration.ipynb) notebook first when setting up a notebook library on a new machine or in a new environment. It configures your notebook library to connect to an Azure Machine Learning workspace, and sets up your workspace and compute to be used by many of the other examples. 
-This [index](.index.md) should assist in navigating the Azure Machine Learning notebook samples and encourage efficient retrieval of topics and content. 
+This [index](./index.md) should assist in navigating the Azure Machine Learning notebook samples and encourage efficient retrieval of topics and content. 
 
 If you want to...
 
diff --git a/configuration.ipynb b/configuration.ipynb
index cf1e9a12..5d12562d 100644
--- a/configuration.ipynb
+++ b/configuration.ipynb
@@ -103,7 +103,7 @@
       "source": [
         "import azureml.core\n",
         "\n",
-        "print(\"This notebook was created using version 1.1.1rc0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.1.2rc0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
diff --git a/how-to-use-azureml/automated-machine-learning/automl_env.yml b/how-to-use-azureml/automated-machine-learning/automl_env.yml
index 42906d19..c907ce70 100644
--- a/how-to-use-azureml/automated-machine-learning/automl_env.yml
+++ b/how-to-use-azureml/automated-machine-learning/automl_env.yml
@@ -28,7 +28,6 @@ dependencies:
   - azureml-contrib-interpret
   - pytorch-transformers==1.0.0
   - spacy==2.1.8
-  - joblib
   - onnxruntime==1.0.0
   - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
 
diff --git a/how-to-use-azureml/automated-machine-learning/automl_env_mac.yml b/how-to-use-azureml/automated-machine-learning/automl_env_mac.yml
index fca456e0..11841b72 100644
--- a/how-to-use-azureml/automated-machine-learning/automl_env_mac.yml
+++ b/how-to-use-azureml/automated-machine-learning/automl_env_mac.yml
@@ -29,7 +29,6 @@ dependencies:
   - azureml-contrib-interpret
   - pytorch-transformers==1.0.0
   - spacy==2.1.8
-  - joblib
   - onnxruntime==1.0.0
   - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz  
 
diff --git a/how-to-use-azureml/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.ipynb b/how-to-use-azureml/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.ipynb
index 63c930e9..6a32356d 100644
--- a/how-to-use-azureml/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.ipynb
@@ -320,7 +320,6 @@
         "|**n_cross_validations**|Number of cross validation splits.|\n",
         "|**training_data**|Input dataset, containing both features and label column.|\n",
         "|**label_column_name**|The name of the label column.|\n",
-        "|**model_explainability**|Indicate to explain each trained pipeline or not.|\n",
         "\n",
         "**_You can find more information about primary metrics_** [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#primary-metric)"
       ]
@@ -352,7 +351,6 @@
         "                             training_data = train_data,\n",
         "                             label_column_name = label,\n",
         "                             validation_data = validation_dataset,\n",
-        "                             model_explainability=True,\n",
         "                             **automl_settings\n",
         "                            )"
       ]
@@ -500,11 +498,11 @@
       "outputs": [],
       "source": [
         "# Wait for the best model explanation run to complete\n",
-        "from azureml.train.automl.run import AutoMLRun\n",
+        "from azureml.core.run import Run\n",
         "model_explainability_run_id = remote_run.get_properties().get('ModelExplainRunId')\n",
         "print(model_explainability_run_id)\n",
         "if model_explainability_run_id is not None:\n",
-        "    model_explainability_run = AutoMLRun(experiment=experiment, run_id=model_explainability_run_id)\n",
+        "    model_explainability_run = Run(experiment=experiment, run_id=model_explainability_run_id)\n",
         "    model_explainability_run.wait_for_completion()\n",
         "\n",
         "# Get the best run object\n",
diff --git a/how-to-use-azureml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.ipynb b/how-to-use-azureml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.ipynb
index 1d66116e..24f56a37 100644
--- a/how-to-use-azureml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.ipynb
@@ -343,7 +343,7 @@
       "outputs": [],
       "source": [
         "from azureml.train.automl import AutoMLConfig\n",
-        "from azureml.train.automl import AutoMLStep\n",
+        "from azureml.pipeline.steps import AutoMLStep\n",
         "\n",
         "automl_settings = {\n",
         "    \"iteration_timeout_minutes\": 10,\n",
diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-high-frequency/automl-forecasting-function.ipynb b/how-to-use-azureml/automated-machine-learning/forecasting-high-frequency/automl-forecasting-function.ipynb
index b5188f21..995facff 100644
--- a/how-to-use-azureml/automated-machine-learning/forecasting-high-frequency/automl-forecasting-function.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-high-frequency/automl-forecasting-function.ipynb
@@ -459,8 +459,8 @@
         "# use forecast_quantiles function, not the forecast() one\n",
         "y_pred_quantiles =  fitted_model.forecast_quantiles(X_test)\n",
         "\n",
-        "# it all nicely aligns column-wise\n",
-        "pd.concat([X_test.reset_index(), y_pred_quantiles], axis=1)"
+        "# quantile forecasts returned in a Dataframe along with the time and grain columns \n",
+        "y_pred_quantiles"
       ]
     },
     {
diff --git a/how-to-use-azureml/automated-machine-learning/regression-hardware-performance-explanation-and-featurization/auto-ml-regression-hardware-performance-explanation-and-featurization.ipynb b/how-to-use-azureml/automated-machine-learning/regression-hardware-performance-explanation-and-featurization/auto-ml-regression-hardware-performance-explanation-and-featurization.ipynb
index 124d0ab1..3935e0b3 100644
--- a/how-to-use-azureml/automated-machine-learning/regression-hardware-performance-explanation-and-featurization/auto-ml-regression-hardware-performance-explanation-and-featurization.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/regression-hardware-performance-explanation-and-featurization/auto-ml-regression-hardware-performance-explanation-and-featurization.ipynb
@@ -514,7 +514,7 @@
         "    content = cefr.read()\n",
         "\n",
         "# Replace the values in train_explainer.py file with the appropriate values\n",
-        "content = content.replace('<<experimnet_name>>', automl_run.experiment.name) # your experiment name.\n",
+        "content = content.replace('<<experiment_name>>', automl_run.experiment.name) # your experiment name.\n",
         "content = content.replace('<<run_id>>', automl_run.id) # Run-id of the AutoML run for which you want to explain the model.\n",
         "content = content.replace('<<target_column_name>>', 'ERP') # Your target column name\n",
         "content = content.replace('<<task>>', 'regression') # Training task type\n",
diff --git a/how-to-use-azureml/automated-machine-learning/regression-hardware-performance-explanation-and-featurization/train_explainer.py b/how-to-use-azureml/automated-machine-learning/regression-hardware-performance-explanation-and-featurization/train_explainer.py
index 9d3b8ca5..5c7789aa 100644
--- a/how-to-use-azureml/automated-machine-learning/regression-hardware-performance-explanation-and-featurization/train_explainer.py
+++ b/how-to-use-azureml/automated-machine-learning/regression-hardware-performance-explanation-and-featurization/train_explainer.py
@@ -22,7 +22,7 @@ run = Run.get_context()
 ws = run.experiment.workspace
 
 # Get the AutoML run object from the experiment name and the workspace
-experiment = Experiment(ws, '<<experimnet_name>>')
+experiment = Experiment(ws, '<<experiment_name>>')
 automl_run = Run(experiment=experiment, run_id='<<run_id>>')
 
 # Check if this AutoML model is explainable
diff --git a/how-to-use-azureml/deployment/production-deploy-to-aks-gpu/production-deploy-to-aks-gpu.ipynb b/how-to-use-azureml/deployment/production-deploy-to-aks-gpu/production-deploy-to-aks-gpu.ipynb
new file mode 100644
index 00000000..adbd1f3e
--- /dev/null
+++ b/how-to-use-azureml/deployment/production-deploy-to-aks-gpu/production-deploy-to-aks-gpu.ipynb
@@ -0,0 +1,314 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+        "\n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/production-deploy-to-aks-gpu/production-deploy-to-aks-gpu.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Deploying a web service to Azure Kubernetes Service (AKS)\n",
+        "This notebook shows the steps for deploying a service: registering a model, creating an image, provisioning a cluster (one time action), and deploying a service to it. \n",
+        "We then test and delete the service, image and model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import azureml.core\n",
+        "print(azureml.core.VERSION)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Get workspace\n",
+        "Load existing workspace from the config file info."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.workspace import Workspace\n",
+        "\n",
+        "ws = Workspace.from_config()\n",
+        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Register the model\n",
+        "Register an existing trained model, add descirption and tags. Prior to registering the model, you should have a TensorFlow [Saved Model](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md) in the `resnet50` directory. You can download a [pretrained resnet50](http://download.tensorflow.org/models/official/20181001_resnet/savedmodels/resnet_v1_fp32_savedmodel_NCHW_jpg.tar.gz) and unpack it to that directory."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "#Register the model\n",
+        "from azureml.core.model import Model\n",
+        "model = Model.register(model_path = \"resnet50\", # this points to a local file\n",
+        "                       model_name = \"resnet50\", # this is the name the model is registered as\n",
+        "                       tags = {'area': \"Image classification\", 'type': \"classification\"},\n",
+        "                       description = \"Image classification trained on Imagenet Dataset\",\n",
+        "                       workspace = ws)\n",
+        "\n",
+        "print(model.name, model.description, model.version)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Provision the AKS Cluster\n",
+        "This is a one time setup. You can reuse this cluster for multiple deployments after it has been created. If you delete the cluster or the resource group that contains it, then you would have to recreate it."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.compute import ComputeTarget, AksCompute\n",
+        "from azureml.core.compute_target import ComputeTargetException\n",
+        "\n",
+        "# Choose a name for your GPU cluster\n",
+        "gpu_cluster_name = \"aks-gpu-cluster\"\n",
+        "\n",
+        "# Verify that cluster does not exist already\n",
+        "try:\n",
+        "    gpu_cluster = ComputeTarget(workspace=ws, name=gpu_cluster_name)\n",
+        "    print(\"Found existing gpu cluster\")\n",
+        "except ComputeTargetException:\n",
+        "    print(\"Creating new gpu-cluster\")\n",
+        "    \n",
+        "    # Specify the configuration for the new cluster\n",
+        "    compute_config = AksCompute.provisioning_configuration(cluster_purpose=AksCompute.ClusterPurpose.DEV_TEST,\n",
+        "                                                           agent_count=1,\n",
+        "                                                           vm_size=\"Standard_NV6\")\n",
+        "    # Create the cluster with the specified name and configuration\n",
+        "    gpu_cluster = ComputeTarget.create(ws, gpu_cluster_name, compute_config)\n",
+        "\n",
+        "    # Wait for the cluster to complete, show the output log\n",
+        "    gpu_cluster.wait_for_completion(show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Deploy the model as a web service to AKS\n",
+        "\n",
+        "First create a scoring script"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%%writefile score.py\n",
+        "import tensorflow as tf\n",
+        "import numpy as np\n",
+        "import json\n",
+        "import os\n",
+        "from azureml.contrib.services.aml_request import AMLRequest, rawhttp\n",
+        "from azureml.contrib.services.aml_response import AMLResponse\n",
+        "\n",
+        "def init():\n",
+        "    global session\n",
+        "    global input_name\n",
+        "    global output_name\n",
+        "    \n",
+        "    session = tf.Session()\n",
+        "\n",
+        "    # AZUREML_MODEL_DIR is an environment variable created during deployment.\n",
+        "    # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n",
+        "    # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n",
+        "    model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'resnet50')\n",
+        "    model = tf.saved_model.loader.load(session, ['serve'], model_path)\n",
+        "    if len(model.signature_def['serving_default'].inputs) > 1:\n",
+        "        raise ValueError(\"This score.py only supports one input\")\n",
+        "    input_name = [tensor.name for tensor in model.signature_def['serving_default'].inputs.values()][0]\n",
+        "    output_name = [tensor.name for tensor in model.signature_def['serving_default'].outputs.values()]\n",
+        "    \n",
+        "\n",
+        "@rawhttp\n",
+        "def run(request):\n",
+        "    if request.method == 'POST':\n",
+        "        reqBody = request.get_data(False)\n",
+        "        resp = score(reqBody)\n",
+        "        return AMLResponse(resp, 200)\n",
+        "    if request.method == 'GET':\n",
+        "        respBody = str.encode(\"GET is not supported\")\n",
+        "        return AMLResponse(respBody, 405)\n",
+        "    return AMLResponse(\"bad request\", 500)\n",
+        "\n",
+        "def score(data):\n",
+        "    result = session.run(output_name, {input_name: [data]})\n",
+        "    return json.dumps(result[1].tolist())\n",
+        "\n",
+        "if __name__ == \"__main__\":\n",
+        "    init()\n",
+        "    with open(\"test_image.jpg\", 'rb') as f:\n",
+        "        content = f.read()\n",
+        "        print(score(content))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now create the deployment configuration objects and deploy the model as a webservice."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Set the web service configuration (using default here)\n",
+        "from azureml.core.model import InferenceConfig\n",
+        "from azureml.core.webservice import AksWebservice\n",
+        "from azureml.core.conda_dependencies import CondaDependencies\n",
+        "from azureml.core.environment import Environment, DEFAULT_GPU_IMAGE\n",
+        "\n",
+        "env = Environment('deploytocloudenv')\n",
+        "# Please see [Azure ML Containers repository](https://github.com/Azure/AzureML-Containers#featured-tags)\n",
+        "# for open-sourced GPU base images.\n",
+        "env.docker.base_image = DEFAULT_GPU_IMAGE\n",
+        "env.python.conda_dependencies = CondaDependencies.create(conda_packages=['tensorflow-gpu==1.12.0','numpy'],\n",
+        "                                 pip_packages=['azureml-contrib-services', 'azureml-defaults'])\n",
+        "\n",
+        "inference_config = InferenceConfig(entry_script=\"score.py\", environment=env)\n",
+        "aks_config = AksWebservice.deploy_configuration()\n",
+        "\n",
+        "# # Enable token auth and disable (key) auth on the webservice\n",
+        "# aks_config = AksWebservice.deploy_configuration(token_auth_enabled=True, auth_enabled=False)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%%time\n",
+        "aks_service_name ='gpu-rn50'\n",
+        "\n",
+        "aks_service = Model.deploy(workspace=ws,\n",
+        "                           name=aks_service_name,\n",
+        "                           models=[model],\n",
+        "                           inference_config=inference_config,\n",
+        "                           deployment_config=aks_config,\n",
+        "                           deployment_target=gpu_cluster)\n",
+        "\n",
+        "aks_service.wait_for_deployment(show_output = True)\n",
+        "print(aks_service.state)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Test the web service\n",
+        "We test the web sevice by passing the test images content."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%%time\n",
+        "import requests\n",
+        "\n",
+        "# if (key) auth is enabled, fetch keys and include in the request\n",
+        "key1, key2 = aks_service.get_keys()\n",
+        "\n",
+        "headers = {'Content-Type':'application/json', 'Authorization': 'Bearer ' + key1}\n",
+        "\n",
+        "# # if token auth is enabled, fetch token and include in the request\n",
+        "# access_token, fetch_after = aks_service.get_token()\n",
+        "# headers = {'Content-Type':'application/json', 'Authorization': 'Bearer ' + access_token}\n",
+        "\n",
+        "test_sample = open('snowleopardgaze.jpg', 'rb').read()\n",
+        "resp = requests.post(aks_service.scoring_uri, test_sample, headers=headers)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Clean up\n",
+        "Delete the service, image, model and compute target"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%%time\n",
+        "aks_service.delete()\n",
+        "model.delete()\n",
+        "gpu_cluster.delete()\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "aashishb"
+      }
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.6"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/how-to-use-azureml/deployment/production-deploy-to-aks-gpu/production-deploy-to-aks-gpu.yml b/how-to-use-azureml/deployment/production-deploy-to-aks-gpu/production-deploy-to-aks-gpu.yml
new file mode 100644
index 00000000..c2afb644
--- /dev/null
+++ b/how-to-use-azureml/deployment/production-deploy-to-aks-gpu/production-deploy-to-aks-gpu.yml
@@ -0,0 +1,5 @@
+name: production-deploy-to-aks-gpu
+dependencies:
+- pip:
+  - azureml-sdk
+  - tensorflow
diff --git a/how-to-use-azureml/deployment/production-deploy-to-aks-gpu/snowleopardgaze.jpg b/how-to-use-azureml/deployment/production-deploy-to-aks-gpu/snowleopardgaze.jpg
new file mode 100644
index 00000000..80450160
Binary files /dev/null and b/how-to-use-azureml/deployment/production-deploy-to-aks-gpu/snowleopardgaze.jpg differ
diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-automated-machine-learning-step.ipynb b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-automated-machine-learning-step.ipynb
index cb049d7b..7b1017c6 100644
--- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-automated-machine-learning-step.ipynb
+++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-automated-machine-learning-step.ipynb
@@ -76,7 +76,7 @@
         "from azureml.core.runconfig import RunConfiguration\n",
         "from azureml.core.conda_dependencies import CondaDependencies\n",
         "\n",
-        "from azureml.train.automl.runtime import AutoMLStep\n",
+        "from azureml.pipeline.steps import AutoMLStep\n",
         "\n",
         "# Check core SDK version number\n",
         "print(\"SDK version:\", azureml.core.VERSION)"
@@ -173,12 +173,7 @@
       "source": [
         "# create a new RunConfig object\n",
         "conda_run_config = RunConfiguration(framework=\"python\")\n",
-        "\n",
-        "conda_run_config.environment.docker.enabled = True\n",
-        "conda_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
-        "\n",
-        "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], \n",
-        "                              conda_packages=['numpy', 'py-xgboost<=0.80'])\n",
+        "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'])\n",
         "conda_run_config.environment.python.conda_dependencies = cd\n",
         "\n",
         "print('run config is ready')"
diff --git a/how-to-use-azureml/track-and-monitor-experiments/logging-api/logging-api.ipynb b/how-to-use-azureml/track-and-monitor-experiments/logging-api/logging-api.ipynb
index e4019e75..80956a09 100644
--- a/how-to-use-azureml/track-and-monitor-experiments/logging-api/logging-api.ipynb
+++ b/how-to-use-azureml/track-and-monitor-experiments/logging-api/logging-api.ipynb
@@ -100,7 +100,7 @@
         "\n",
         "# Check core SDK version number\n",
         "\n",
-        "print(\"This notebook was created using SDK version 1.1.1rc0, you are currently running version\", azureml.core.VERSION)"
+        "print(\"This notebook was created using SDK version 1.1.2rc0, you are currently running version\", azureml.core.VERSION)"
       ]
     },
     {
diff --git a/how-to-use-azureml/track-and-monitor-experiments/tensorboard/tensorboard.ipynb b/how-to-use-azureml/track-and-monitor-experiments/tensorboard/tensorboard.ipynb
index aa283715..83ff13b5 100644
--- a/how-to-use-azureml/track-and-monitor-experiments/tensorboard/tensorboard.ipynb
+++ b/how-to-use-azureml/track-and-monitor-experiments/tensorboard/tensorboard.ipynb
@@ -145,9 +145,12 @@
         "import requests\n",
         "import os\n",
         "\n",
-        "tf_code = requests.get(\"https://raw.githubusercontent.com/tensorflow/tensorflow/r1.8/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py\")\n",
+        "tf_code = requests.get(\"https://raw.githubusercontent.com/tensorflow/tensorflow/r2.1/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py\")\n",
+        "input_code = requests.get(\"https://raw.githubusercontent.com/tensorflow/tensorflow/r2.1/tensorflow/examples/tutorials/mnist/input_data.py\")\n",
         "with open(os.path.join(exp_dir, \"mnist_with_summaries.py\"), \"w\") as file:\n",
-        "    file.write(tf_code.text)"
+        "    file.write(tf_code.text.replace(\"from tensorflow.examples.tutorials.mnist import input_data\", \"import input_data\"))\n",
+        "with open(os.path.join(exp_dir, \"input_data.py\"), \"w\") as file:\n",
+        "    file.write(input_code.text)"
       ]
     },
     {
@@ -186,7 +189,7 @@
         "from azureml.core import Experiment\n",
         "from azureml.core.script_run_config import ScriptRunConfig\n",
         "\n",
-        "logs_dir = os.path.join(os.curdir, \"logs\")\n",
+        "logs_dir = os.path.join(os.curdir, os.path.join(\"logs\", \"tb-logs\"))\n",
         "data_dir = os.path.abspath(os.path.join(os.curdir, \"mnist_data\"))\n",
         "\n",
         "if not path.exists(data_dir):\n",
@@ -334,7 +337,8 @@
         "tf_estimator = TensorFlow(source_directory=exp_dir,\n",
         "                          compute_target=attached_dsvm_compute,\n",
         "                          entry_script='mnist_with_summaries.py',\n",
-        "                          script_params=script_params)\n",
+        "                          script_params=script_params,\n",
+        "                          framework_version=\"2.0\")\n",
         "\n",
         "run = exp.submit(tf_estimator)\n",
         "\n",
@@ -396,17 +400,16 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
-        "\n",
+        "from azureml.core.compute import AmlCompute\n",
         "# choose a name for your cluster\n",
-        "cluster_name = \"cpucluster\"\n",
+        "cluster_name = \"cpu-cluster\"\n",
         "\n",
         "cts = ws.compute_targets\n",
         "found = False\n",
         "if cluster_name in cts and cts[cluster_name].type == 'AmlCompute':\n",
-        "   found = True\n",
-        "   print('Found existing compute target.')\n",
-        "   compute_target = cts[cluster_name]\n",
+        "    found = True\n",
+        "    print('Found existing compute target.')\n",
+        "    compute_target = cts[cluster_name]\n",
         "if not found:\n",
         "    print('Creating a new compute target...')\n",
         "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2', \n",
@@ -444,7 +447,8 @@
         "tf_estimator = TensorFlow(source_directory=exp_dir,\n",
         "                          compute_target=compute_target,\n",
         "                          entry_script='mnist_with_summaries.py',\n",
-        "                          script_params=script_params)\n",
+        "                          script_params=script_params,\n",
+        "                          framework_version=\"2.0\")\n",
         "\n",
         "run = exp.submit(tf_estimator)\n",
         "\n",
@@ -539,6 +543,24 @@
         "name": "roastala"
       }
     ],
+    "category": "training",
+    "compute": [
+      "Local",
+      "DSVM",
+      "AML Compute"
+    ],
+    "datasets": [
+      "None"
+    ],
+    "deployment": [
+      "None"
+    ],
+    "exclude_from_index": false,
+    "framework": [
+      "TensorFlow"
+    ],
+    "friendly_name": "Tensorboard integration with run history",
+    "index_order": 3,
     "kernelspec": {
       "display_name": "Python 3.6",
       "language": "python",
@@ -556,28 +578,10 @@
       "pygments_lexer": "ipython3",
       "version": "3.6.6"
     },
-    "friendly_name": "Tensorboard integration with run history",
-    "exclude_from_index": false,
-    "index_order": 3,
-    "category": "training",
-    "task": "Run a TensorFlow job and view its Tensorboard output live",
-    "datasets": [
-      "None"
-    ],
-    "compute": [
-      "Local",
-      "DSVM",
-      "AML Compute"
-    ],
-    "deployment": [
-      "None"
-    ],
-    "framework": [
-      "TensorFlow"
-    ],
     "tags": [
       "None"
-    ]
+    ],
+    "task": "Run a TensorFlow job and view its Tensorboard output live"
   },
   "nbformat": 4,
   "nbformat_minor": 2
diff --git a/how-to-use-azureml/track-and-monitor-experiments/tensorboard/tensorboard.yml b/how-to-use-azureml/track-and-monitor-experiments/tensorboard/tensorboard.yml
index cd0618eb..024d3600 100644
--- a/how-to-use-azureml/track-and-monitor-experiments/tensorboard/tensorboard.yml
+++ b/how-to-use-azureml/track-and-monitor-experiments/tensorboard/tensorboard.yml
@@ -3,4 +3,5 @@ dependencies:
 - pip:
   - azureml-sdk
   - azureml-tensorboard
-  - tensorflow<1.15
+  - tensorflow
+  - setuptools>=41.0.0
diff --git a/how-to-use-azureml/training-with-deep-learning/export-run-history-to-tensorboard/export-run-history-to-tensorboard.yml b/how-to-use-azureml/training-with-deep-learning/export-run-history-to-tensorboard/export-run-history-to-tensorboard.yml
index 6f79f760..17aa9f1d 100644
--- a/how-to-use-azureml/training-with-deep-learning/export-run-history-to-tensorboard/export-run-history-to-tensorboard.yml
+++ b/how-to-use-azureml/training-with-deep-learning/export-run-history-to-tensorboard/export-run-history-to-tensorboard.yml
@@ -3,7 +3,8 @@ dependencies:
 - pip:
   - azureml-sdk
   - azureml-tensorboard
-  - tensorflow<1.15.0
+  - tensorflow
   - tqdm
   - scipy
   - sklearn
+  - setuptools>=41.0.0
diff --git a/how-to-use-azureml/work-with-data/datasets-tutorial/labeled-datasets/labeled-datasets.yml b/how-to-use-azureml/work-with-data/datasets-tutorial/labeled-datasets/labeled-datasets.yml
deleted file mode 100644
index 26c3266a..00000000
--- a/how-to-use-azureml/work-with-data/datasets-tutorial/labeled-datasets/labeled-datasets.yml
+++ /dev/null
@@ -1,10 +0,0 @@
-name: labeled-datasets
-dependencies:
-- pip:
-  - azureml-sdk
-  - azureml-dataprep
-  - pandas
-  - fuse
-  - azureml.contrib.dataset
-  - matplotlib
-  - torchvision
diff --git a/how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/pipeline-for-image-classification.ipynb b/how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/pipeline-for-image-classification.ipynb
index eefd2b70..739fca0e 100644
--- a/how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/pipeline-for-image-classification.ipynb
+++ b/how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/pipeline-for-image-classification.ipynb
@@ -141,7 +141,7 @@
         "from azureml.core.compute_target import ComputeTargetException\n",
         "\n",
         "# choose a name for your cluster\n",
-        "cluster_name = \"your-cluster-name\"\n",
+        "cluster_name = \"gpu-cluster\"\n",
         "\n",
         "try:\n",
         "    compute_target = ComputeTarget(workspace=workspace, name=cluster_name)\n",
diff --git a/how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/pipeline-for-image-classification.yml b/how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/pipeline-for-image-classification.yml
index a7379445..e6b3df70 100644
--- a/how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/pipeline-for-image-classification.yml
+++ b/how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/pipeline-for-image-classification.yml
@@ -3,5 +3,5 @@ dependencies:
 - pip:
   - azureml-sdk
   - azureml-dataprep
-  - pandas
+  - pandas<=0.23.4
   - fuse
diff --git a/how-to-use-azureml/work-with-data/datasets-tutorial/timeseries-datasets/tabular-timeseries-dataset-filtering.ipynb b/how-to-use-azureml/work-with-data/datasets-tutorial/timeseries-datasets/tabular-timeseries-dataset-filtering.ipynb
index 7f909acd..fc4cdd9e 100644
--- a/how-to-use-azureml/work-with-data/datasets-tutorial/timeseries-datasets/tabular-timeseries-dataset-filtering.ipynb
+++ b/how-to-use-azureml/work-with-data/datasets-tutorial/timeseries-datasets/tabular-timeseries-dataset-filtering.ipynb
@@ -543,7 +543,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "ylxiong"
+        "name": "jamgan"
       }
     ],
     "category": "tutorial",
diff --git a/how-to-use-azureml/work-with-data/datasets-tutorial/timeseries-datasets/tabular-timeseries-dataset-filtering.yml b/how-to-use-azureml/work-with-data/datasets-tutorial/timeseries-datasets/tabular-timeseries-dataset-filtering.yml
index 96daf4fb..a3471ade 100644
--- a/how-to-use-azureml/work-with-data/datasets-tutorial/timeseries-datasets/tabular-timeseries-dataset-filtering.yml
+++ b/how-to-use-azureml/work-with-data/datasets-tutorial/timeseries-datasets/tabular-timeseries-dataset-filtering.yml
@@ -3,4 +3,4 @@ dependencies:
 - pip:
   - azureml-sdk
   - azureml-dataprep
-  - pandas
+  - pandas<=0.23.4
diff --git a/how-to-use-azureml/work-with-data/datasets-tutorial/train-with-datasets/train-with-datasets.ipynb b/how-to-use-azureml/work-with-data/datasets-tutorial/train-with-datasets/train-with-datasets.ipynb
index 5dfbff64..9e477a3d 100644
--- a/how-to-use-azureml/work-with-data/datasets-tutorial/train-with-datasets/train-with-datasets.ipynb
+++ b/how-to-use-azureml/work-with-data/datasets-tutorial/train-with-datasets/train-with-datasets.ipynb
@@ -529,8 +529,9 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(run.get_metrics())\n",
-        "metrics = run.get_metrics()"
+        "run.wait_for_completion()\n",
+        "metrics = run.get_metrics()\n",
+        "print(metrics)"
       ]
     },
     {
diff --git a/how-to-use-azureml/work-with-data/datasets-tutorial/train-with-datasets/train-with-datasets.yml b/how-to-use-azureml/work-with-data/datasets-tutorial/train-with-datasets/train-with-datasets.yml
index 096a82b1..4f490f41 100644
--- a/how-to-use-azureml/work-with-data/datasets-tutorial/train-with-datasets/train-with-datasets.yml
+++ b/how-to-use-azureml/work-with-data/datasets-tutorial/train-with-datasets/train-with-datasets.yml
@@ -4,6 +4,6 @@ dependencies:
   - azureml-sdk
   - azureml-widgets
   - azureml-dataprep
-  - pandas
+  - pandas<=0.23.4
   - fuse
   - scikit-learn
diff --git a/index.md b/index.md
index 12511149..797684dd 100644
--- a/index.md
+++ b/index.md
@@ -117,6 +117,7 @@ Machine Learning notebook samples and encourage efficient retrieval of topics an
 | [enable-app-insights-in-production-service](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/enable-app-insights-in-production-service/enable-app-insights-in-production-service.ipynb) |  |  |  |  |  |  |
 | [onnx-model-register-and-deploy](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-model-register-and-deploy.ipynb) |  |  |  |  |  |  |
 | [production-deploy-to-aks](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb) |  |  |  |  |  |  |
+| [production-deploy-to-aks-gpu](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/production-deploy-to-aks-gpu/production-deploy-to-aks-gpu.ipynb) |  |  |  |  |  |  |
 | [tensorflow-model-register-and-deploy](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/tensorflow/tensorflow-model-register-and-deploy.ipynb) |  |  |  |  |  |  |
 | [explain-model-on-amlcompute](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/explain-model/azure-integration/remote-explanation/explain-model-on-amlcompute.ipynb) |  |  |  |  |  |  |
 | [save-retrieve-explanations-run-history](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/explain-model/azure-integration/run-history/save-retrieve-explanations-run-history.ipynb) |  |  |  |  |  |  |
diff --git a/setup-environment/configuration.ipynb b/setup-environment/configuration.ipynb
index 62435abd..a849870b 100644
--- a/setup-environment/configuration.ipynb
+++ b/setup-environment/configuration.ipynb
@@ -102,7 +102,7 @@
       "source": [
         "import azureml.core\n",
         "\n",
-        "print(\"This notebook was created using version 1.1.1rc0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.1.2rc0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
diff --git a/tutorials/README.md b/tutorials/README.md
index c861e7bc..53b6e975 100644
--- a/tutorials/README.md
+++ b/tutorials/README.md
@@ -28,7 +28,6 @@ The following tutorials are intended to provide examples of more advanced featur
 | Tutorial | Description | Notebook | Task | Framework | 
 | --- | --- | --- | --- | --- |
 | [Build an Azure Machine Learning pipeline for batch scoring](https://docs.microsoft.com/azure/machine-learning/tutorial-pipeline-batch-scoring-classification) | Create an Azure Machine Learning pipeline to run batch scoring image classification jobs | [tutorial-pipeline-batch-scoring-classification.ipynb](machine-learning-pipelines-advanced/tutorial-pipeline-batch-scoring-classification.ipynb) | Image Classification | TensorFlow
-Complete these tutorials to learn how to train and deploy models using Azure Machine Learning services and Python SDK. These Notebooks accompany the tutorial articles for:
 
 For additional documentation and resources, see the [official documentation site for Azure Machine Learning](https://docs.microsoft.com/azure/machine-learning/).
 
diff --git a/tutorials/image-classification-mnist-data/img-classification-part1-training.ipynb b/tutorials/image-classification-mnist-data/img-classification-part1-training.ipynb
index 65db573c..73cc8d79 100644
--- a/tutorials/image-classification-mnist-data/img-classification-part1-training.ipynb
+++ b/tutorials/image-classification-mnist-data/img-classification-part1-training.ipynb
@@ -126,7 +126,8 @@
       "metadata": {},
       "source": [
         "### Create or Attach existing compute resource\n",
-        "By using Azure Machine Learning Compute, a managed service, data scientists can train machine learning models on clusters of Azure virtual machines. Examples include VMs with GPU support. In this tutorial, you create Azure Machine Learning Compute as your training environment. The code below creates the compute clusters for you if they don't already exist in your workspace.\n",
+        "By using Azure Machine Learning Compute, a managed service, data scientists can train machine learning models on clusters of Azure virtual machines. Examples include VMs with GPU support. In this tutorial, you create Azure Machine Learning Compute as your training environment. You will submit Python code to run on this VM later in the tutorial. \n",
+        "The code below creates the compute clusters for you if they don't already exist in your workspace.\n",
         "\n",
         "**Creation of compute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace the code will skip the creation process."
       ]
@@ -263,7 +264,7 @@
       "source": [
         "## Train on a remote cluster\n",
         "\n",
-        "For this task, submit the job to the remote training cluster you set up earlier.  To submit a job you:\n",
+        "For this task, you submit the job to run on the remote training cluster you set up earlier.  To submit a job you:\n",
         "* Create a directory\n",
         "* Create a training script\n",
         "* Create an estimator object\n",