Updated graphics

SQLServerMLServices/notebooks/01-SQL Server Machine Learning Services Architecture.ipynb

@@ -62,7 +62,7 @@
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-            "source": "When the R or Python language is called via a special Stored Procedure (which you must enable) SQL Server transfers data to the R or Python process which runs the code, and returns the result to the Stored Procedure in SQL Server.\n\n<p>\n<img src=\"https://github.com/Microsoft/sqlworkshops/blob/master/graphics/TSQLAndR.png?raw=true\" width=\"500\">\n<p>\n\nYou can run code to execute in Python as well as R. You can use either language by simply setting a parameter in the Stored Procedure.\n\nThis allows SQL Server professionals to work with and hybrid data in the way they are familiar with, and the Data Scientist to develop their R or Python code anywhere, and then deploying that code to SQL Server by embedding it in a Stored Procedure.\n\nRun a few statements that implement this process:",
+            "source": "When the R or Python language is called via a special Stored Procedure (which you must enable) SQL Server transfers data to the R or Python process which runs the code, and returns the result to the Stored Procedure in SQL Server.\n\n<p>\n<img src=\"https://github.com/amthomas46/SQL/blob/master/sql-cs-icc/code/sql-notebooks/images/java-r-python.png?raw=true\" width=\"500\">\n\nHere's a breakdown of the code:\n\n<p>\n<img src=\"https://github.com/Microsoft/sqlworkshops/blob/master/graphics/TSQLAndR.png?raw=true\" width=\"500\">\n<p>\n\nYou can run code to execute in Python as well as R. You can use either language by simply setting a parameter in the Stored Procedure.\n\nThis allows SQL Server professionals to work with and hybrid data in the way they are familiar with, and the Data Scientist to develop their R or Python code anywhere, and then deploying that code to SQL Server by embedding it in a Stored Procedure.\n\nRun a few statements that implement this process:",
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@@ -93,7 +93,6 @@
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                         "text/html": "<table><tr><th>Is R Working</th></tr><tr><td>1</td></tr></table>"
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                         "text/html": "<table><tr><th>Is Python Working</th></tr><tr><td>1</td></tr></table>"
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SQLServerMLServices/notebooks/05-Phase 3 - Modeling.ipynb

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-            "source": "<p><img style=\"float: left; margin: 0px 15px 15px 0px;\" src=\"https://github.com/Microsoft/sqlworkshops/blob/master/graphics/textbubble.png?raw=true\"></p>\r\n\r\n<br>\r\n<br>\r\n<br>\r\n\r\nYou're learning to use the Team Data Science Process to create a complete solution, using SQL Server as the platform. The phases in the Team Data Science process are:\r\n\r\n<dl>\r\n  <dt>1 - Business Understanding</dt>\r\n  <dt>2 - Data Acquisition and Understanding</dt>\r\n  <dt>3 - Modeling <i>(This module)</i></dt>\r\n  <dt>4 - Deployment</dt>\r\n  <dt>5 - Customer Acceptance and Model Retraining</dt>\r\n<dl>\r\n\r\n<p style=\"border-bottom: 1px solid lightgrey;\"></p>\r\n\r\n<img style=\"float: left; margin: 0px 15px 15px 0px;\" src=\"https://github.com/Microsoft/sqlworkshops/blob/master/graphics/pin.jpg?raw=true\"><b>3.0 Modeling</b>>\r\n<br>\r\n\r\nIn this phase, you'll perform feature engineering, create the experiment runs, and run experiments with various settings and parameters. After selecting the best performing run, you'll create a trained model and save it for operationalization in the next phase.\r\n\r\n### Goals for Modeling\r\n\r\n- Determine the optimal data features for the machine-learning model.\r\n- Create an informative machine-learning model that predicts the target most accurately.\r\n- Create a machine-learning model that's suitable for production.\r\n\r\n### How to do it\r\n\r\n- Feature engineering: Create data features from the raw data to facilitate model training.\r\n- Model training: Find the model that answers the question most accurately by comparing their success metrics.\r\n- Determine if your model is suitable for production.\r\n",
+            "source": "<p><img style=\"float: left; margin: 0px 15px 15px 0px;\" src=\"https://github.com/Microsoft/sqlworkshops/blob/master/graphics/textbubble.png?raw=true\"></p>\r\n\r\n<br>\r\n<br>\r\n<br>\r\n\r\nYou're learning to use the Team Data Science Process to create a complete solution, using SQL Server as the platform. The phases in the Team Data Science process are:\r\n\r\n<dl>\r\n  <dt>1 - Business Understanding</dt>\r\n  <dt>2 - Data Acquisition and Understanding</dt>\r\n  <dt>3 - Modeling <i>(This module)</i></dt>\r\n  <dt>4 - Deployment</dt>\r\n  <dt>5 - Customer Acceptance and Model Retraining</dt>\r\n<dl>\r\n\r\n<p style=\"border-bottom: 1px solid lightgrey;\"></p>\r\n\r\n<img style=\"float: left; margin: 0px 15px 15px 0px;\" src=\"https://github.com/Microsoft/sqlworkshops/blob/master/graphics/pin.jpg?raw=true\"><b>3.0 Modeling</b>\r\n<br>\r\n\r\nIn this phase, you'll perform feature engineering, create the experiment runs, and run experiments with various settings and parameters. After selecting the best performing run, you'll create a trained model and save it for operationalization in the next phase.\r\n\r\n### Goals for Modeling\r\n\r\n- Determine the optimal data features for the machine-learning model.\r\n- Create an informative machine-learning model that predicts the target most accurately.\r\n- Create a machine-learning model that's suitable for production.\r\n\r\n### How to do it\r\n\r\n- Feature engineering: Create data features from the raw data to facilitate model training.\r\n- Model training: Find the model that answers the question most accurately by comparing their success metrics.\r\n- Determine if your model is suitable for production.\r\n",
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