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z1cf853FE)F7d|s!A0manNb?8(_k| zO^j3msaJxCf(?au^jE(33ORYgwon_{>Gbct5|(yz-I;fXz8I0#R`ez^lKA+WtHqfA z4(fv+t!YAfqyck{{a#U>oSGQt)%GR!u@xSbi$QEd>CLm)fk+D{5OR^9&o@EpQ}PQ0 zfWWH6^mEOVC8UXyU)8>u39_85_wq#-jh`2Isa-kSGGIIIYcngcB=VadI#AK_H*7e= zao~Pgqk-Lq(D=#{&NM{cU{#a^Gykw@nou+x^et(SZNC q+SKeM2^K*7@t;Dg%Fm%ML=^LL4>|AGM&JIs0gw`t7cCdo^ZPG~bH{K1 literal 0 HcmV?d00001 diff --git a/lab01.md b/lab01.md index 453e33b..b4b5064 100644 --- a/lab01.md +++ b/lab01.md @@ -102,7 +102,7 @@ and wish take advantage of a trial environment, visit Graphical user interface, application, Teams Description automatically generated -1. Choose “Import data” and enter a dataset name “CustomerDemographics” and click Next. +1. Choose “Import data” (_Note: the UI has been renamed to "Microsoft Power Query" in the latest release_) and enter a dataset name “CustomerDemographics” and click Next. Graphical user interface, text, application, email Description automatically generated @@ -137,7 +137,7 @@ and wish take advantage of a trial environment, visit Graphical user interface, application, table Description automatically generated -1. Choose to refresh manually (since this is a static sample dataset that +1. Choose "refresh manually" (since this is a static sample dataset that won’t change) and click Save. Graphical user interface, text, application Description automatically generated @@ -149,7 +149,7 @@ and wish take advantage of a trial environment, visit ## Step 3: Load CustomerServiceAttributes sample dataset to Customer Insights -1. Click “Add data source”, Import data, and name it +1. Click “Add data source”, Import data (_Note: the UI has been renamed to "Microsoft Power Query" in the latest release_), and name it CustomerServiceAttributes and click Next: Graphical user interface, text, application, email Description automatically generated diff --git a/lab04.md b/lab04.md index fd22b56..6d298da 100644 --- a/lab04.md +++ b/lab04.md @@ -197,10 +197,7 @@ designing the experiment. 6. In the Advanced Settings, you will see your selections from the prior screen at the top. Name the compute **CustomerChurnCC**. Set the Minimum number of nodes at 0 and Maximum at 2. Set the Idle - seconds before scale down to 60. Click Create when complete. - **Important: This will be mentioned later on as well, but we need to - make sure and scale down our clusters when we’re done to avoid - incurring charges.** + seconds before scale down to 60. Click Create when complete and wait a few minutes. Graphical user interface, text, application Description automatically generated @@ -239,12 +236,16 @@ designing the experiment. 11. Next, we know that TotalCharges has a few blank values (we could have tested this via using the Summarize Data asset and evaluating each column distribution but for the sake of this lab we’re going to - move on to handling them). Drag **Clean Missing Data** onto the page + move on to handling them). Search for **Clean Missing Data**: + + + + Drag **Clean Missing Data** onto the page and connect TelcoChurn to the new asset. A picture containing chart Description automatically generated -12. In the properties pane for Clean Missing Data. Select TotalCharges +12. In the properties pane for Clean Missing Data. Click "Edit column" and select TotalCharges as the Column and set the parameters below. *Note: If the goal of the lab was to build the best model we would have run some form of regression or sub-sample from the complete rows to find a predicted @@ -290,7 +291,7 @@ designing the experiment. 15. The right node of the Split Data asset is connected to another instance of **Select Columns in Dataset**. This extracts the Customer ID’s of the testing data. We are going to join this back in - to the results dataset after the models have run. + to the results dataset after the models have run. _Note that the description "Extract customer ID to join back in later" can be added in the Comment section of the properties pane of that module. This is optional._ Diagram Description automatically generated @@ -315,6 +316,8 @@ designing the experiment. ## Step 4: Training models +_Note: If you get confused on how modules should be connected, feel free to skip down to a zoomed-out screenshot in Step 5.7 below._ + Now that we’ve setup the design of our experiment, we can begin to train the models and evaluate the outputs. diff --git a/lab05.md b/lab05.md index fd1f80a..a24c0e9 100644 --- a/lab05.md +++ b/lab05.md @@ -80,7 +80,7 @@ learning models into Customer Insights. Graphical user interface, text, application, email Description automatically generated -1. Choose Import data and give it the name “AzureMlResults” and click Next. +1. Choose Import data (_Note: the UI has been renamed to "Microsoft Power Query" in the latest release_) and give it the name “AzureMlResults” and click Next. Graphical user interface, text, application, email Description automatically generated diff --git a/lab06.md b/lab06.md index 67ec9df..51156d5 100644 --- a/lab06.md +++ b/lab06.md @@ -209,12 +209,12 @@ flow is executed under the covers on highly scalable Spark compute. -1. Click the Import schema button now. Accepting the default formats is - adequate for our file: +1. Click the Import schema button (which was grayed out in step 13) now. Accepting the default formats is + adequate for our file. Click the Import button at the bottom to accept the defaults: Table Description automatically generated -1. Now the schema for this CustomerChargesDetail source is populated. +1. Wait a few seconds for the schema to be loaded. Now the schema for this CustomerChargesDetail source is populated. Graphical user interface, application Description automatically generated