Machine Learning in the Cloud with Azure Machine Learning
February 2019
Lecture 1: Introduction to Azure Machine Learning
Classification - Predicting a discrete category
Regression - Predicting a value
Anomaly detection - Identifying unusual data. e.g. fraud data.
Clustering - Grouping similar data together
25 different machine learning algorithms on azure
Azure Machine Learning Algorithms
Azure Machine Learning Studio
Drag and drop interface.
+ New -> Blank Experiment -> Saved Datasets -> Samples
Open CV - Open source Computer Vision
Predict doctor show or no-show status
Course materials here: https://tetranoodle.com/course-materials-azure-ml/
+ New -> Dataset -> From local file
Saved Datasets -> My datasets -> Drag file to experiment page -> Right click -> Visualize -> Click on column to see stats -> Hover over histogram to see stats of each bin.
Type 'meta data' in search box. Drag and drop 'Edit Metadata'. Click on circle in bottom and dataset and drag to Edit Metadata box to connect them. -> Click on 'Launch column selector (to the right). Bring in all columns to the right except for AppointmentRegistration.
Drag another 'Edit Metadata' box below and join it. Categorical -> Make categorical -> Launch Column Selector. Select columns to turn into categorical. Fields -> Features
Drag another 'Edit Metadata' box below and join it -> Launch Column Selector. Choose numeric columns. Categorical -> Make non-categorical. Fields -> Features
Drag another 'Edit Metadata' box below and join it -> Launch Column Selector. Choose label columns -> Fields -> Label
Click run button and the bottom. Right click last Edit Metadata -> Results dataset -> visualize
Search 'split' -> Drag and drop 'split data' -> Change 'Fraction of rows in the fir...' to 0.7 (70%)
Search 'train model' -> Drag and drop 'train model'. Drag and drop another 'train model' then connect one dataset to each training model
Machine Learning -> Initialize Model -> Classification -> Drag and drop 'Two-class Decision Forest' as well as 'Two-class Decision Jungle'. Connect these to the Train Models.'
Search 'score' and drag and drop two 'score model' to the canvas. Connect the model to these as well as the test data from 'Split Data'
Search 'evaluate' -> Drag and drop 'Evaluate Model' then connect 'Score Model' to 'Evaluate Model'
Click on 'Train model' then 'Launch Column Selector' -> 'Status'
Click 'Run' button
You can click on the model and adjust parameters on the right
Right click evaluate model and click 'vizualize'. You can click 'ROC', 'PRECISION/RECALL' OR 'LIFT' to see those curves. Click on blue or red box to see the results from those tables.
Setup Web API
Click on Second model -> Set Up Web Service -> Predictive Web Service
Run (model again)
Deploy Web Service
You can download the Excel template which has the API key embedded in it and will allow you to run the API in that
Test -> Choose values -> Ok. Then shows values at bottom e.g. Show-Up and percent confidence
Click on Request/Response and it will show the JSON file as well as the python code to access the API
Predicting house prices
New -> Blank Experiment
New -> Dataset -> From local file
Saved Datasets -> My Datasets
Click on the one at the bottom of the box -> Visualize
Search 'Edit Metadata' -> Drag and drop 'Edit Metadata' -> Connect -> Launch column selector -> Keep all columns except for Id
Drag and drop 'Edit Metadata' -> Connect -> Launch column selector -> Choose categoircal featues 'ok' -> categorical: make categorical.
Drag and drop 'Edit Metadata' -> Connect -> categorical: make non-categorical, Fields: Features
Drag and drop 'Edit Metadata' -> Connect -> Do date-time fields.
Drag and drop 'Split data' -> Connect -> Fraction...: 0.7
Drag and drop 'Train Model' -> Connect -> Choose 'SalePrice' as the thing to predict
Drag and drop 'Bayesian Linear Regression' -> Connect to Train Model
Drag and drop 'Score Model' -> Connect from Train Model and Split Data
Run click 'Score Model' -> Visualize
Deploy a web application with Azure ML
portal.azure.com
+ Create a resource
Type 'Azure ML Request-Response Service Web App' in search box -> create -> Type in an 'App name' -> Type in a 'Resource Group' e.g. appnameRG
Open experiment in ML Studio and go to the Web Service tab -> Request-Response -> Grab the URL next to POST
In Azure you can select 'App Service plan/Location' and choose a location e.g. where you end-users reside.
Click on the 'create' button at the bottom.
You can go to 'Resource groups' or 'App Services' to see the app
Click on the 'App' and click 'Browse'