1. Advanced Analytics with Azure Machine Learning
Microsoft C+E Technology Training
Solution Area
Data Analytics
Solution
Advanced Analytics
Technology
Azure Machine Learning
Session Title
Advanced Analytics with Azure Machine Learning
Buck Woody

2. Learning Objectives Understand Machine Learning
Use the Cortana Intelligence Suite (CIS) to create a Machine Learning Solution
Publish and consume an Azure ML model
At the end of this Module, you will:
Understand Azure ML and how experiments are created
Understand how MRS can be used to perform Machine Learning experiments
Use ADF to schedule Azure ML Activities

3. Course Module List Machine Learning Overview
The Cortana Intelligence Suite
Machine Learning Tools and Setup
Azure ML Studio and the Team Data Science Process
Ingesting and Preparing Data
Algorithms
Model Scoring and Evaluation
Lab
Publishing and Using the Model

4. Machine Learning Overview
What is Machine Learning: us/documentation/articles/machine-learning-what-is- machine-learning/

5. Machine Learning in 5 Minutes
The Formal one:
“A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E.”
A Practical Example:
What is Machine Learning: us/documentation/articles/machine-learning-what-is- machine-learning/
Look at data. Try something. Get the right answer? No? Look at the data. Do something different. Better? Yes? Do that again. (Repeat)

6. Machine Learning Algorithms
Split into two main categories:
Supervised learning
Predicting the future
Learn from known past examples to predict future
Labels provided
Unsupervised learning
Making sense of data
Understanding the past
Learning the structure of data
Labels no provided
Algorithm Documentation:
Exploring:

7. Machine Learning Capabilities
Which category
(Classification)
How much/many
(Regression)
Which group
(Clustering,
Recommender)
Is it odd
(Anomaly)
Which action
(Reinforcement Learning)
Classification: Assign a category to each item (Chinese | French | Indian | Italian | Japanese restaurant). – Which Category?
Regression: Predict a real value for each item (stock/currency value, temperature). – How much/how many?
Clustering/Recommendation: Partition items into homogeneous groups (clustering twitter posts by topic). – Which Groups?
Anomaly: Identify when something unexpected happens. – Is this weird?
Reinforcement Learning: Make an appropriate action for some new data. – Which action?

8. The Cortana Intelligence Suite
Example paths for using Azure ML: us/documentation/articles/machine-learning-data-science- plan-sample-scenarios/

9. Cortana Intelligence in a Sentence:
Cortana Intelligence is a Platform and a Process to perform advanced analytics from start to finish
What you can do with CIS: us/server-cloud/cortana-intelligence-suite/why-cortana- intelligence.aspx
More about the process: Juarez/Understanding-Data-Science-for-building- Predictive-Analytics-Solutions-by-Francesca-Lazzeri
Data Science Blog:

10. The Team Data Science Process
Cross Industry Standard Process for Data Mining
The Team Data Science Process
Consume
Deploying
Train Models
Evaluating
Create Models
Modeling
Generate Features
Data Preparation
This process largely follows the CRISP-DM model:
It also references the Cortana Intelligence process: us/documentation/articles/data-science-process-overview/
A complete process diagram is here: us/documentation/learning-paths/cortana-analytics- process/
Some walkthrough’s of the various services: us/documentation/articles/data-science-process- walkthroughs/
Explore and Visualize
Data Understanding
Planning, Environment, Ingest
Business Understanding
Data Science Blog:

11. The Cortana Intelligence Platform
Cortana, Cognitive Services, Bot Framework
Power BI
Azure Stream Analytics
Azure HDInsight
Azure Machine Learning and MRS
Azure SQL DB, Data Warehouse, DocumentDB
Azure Data Lake
Platform and Storage: Microsoft Azure – Storage: (Host It)
Azure Data Catalog: (Doc It)
Azure Data Factory: (Move It)
Azure Event Hubs: (Bring It)
Azure Data Lake: (Store It)
Azure DocumentDB: , Azure SQL Data Warehouse: warehouse/ (Relate It)
Azure Machine Learning: learning/ (Learn It)
Azure HDInsight: (Scale It)
Azure Stream Analytics: (Stream It)
Power BI: (See It)
Cortana: and-speech-recognition-new-code-samples/  and your-customers-10-by-10/ and  (Say It)
Cognitive Services:
Bot Framework:
Azure Event Hubs
Azure Data Factory
Azure Data Catalog
Microsoft Azure

12. Machine Learning Tools and Setup
Data Science for Beginners: us/documentation/articles/machine-learning-data- science-for-beginners-the-5-questions-data-science- answers/

13. The Azure ML Environment
Development Environment
Creating Experiments
Sharing a Workspace
Deployment Environment
Publishing the Model
Using the API
Consuming in various tools
The Azure Machine Learning Studio: us/documentation/articles/machine-learning-what-is-ml- studio/
Guided tutorials: us/documentation/services/machine-learning/
Microsoft Azure Virtual Academy course: courses/microsoft-azure-machine-learning-jump-start- 8425?l=ehQZFoKz_

14. Azure ML Elements Import Data Preprocess Algorithm Split Data
Designing an experiment in the Studio: us/documentation/articles/machine-learning-what-is-ml- studio/
Train Model
Score Model

15. Azure ML Studio and the Team Data Science Process
Walkthroughs of each step: us/documentation/learning-paths/data-science-process/

16. Creating an Experiment
Create
Workspace
Deploy Model
Consume Model
Build and Model
Get/Prepare Data
Build/Edit Experiment
Create/Update Model
Evaluate Model Results
Beginning Series: us/documentation/articles/machine-learning-data- science-for-beginners-the-5-questions-data-science- answers/

17. Ingesting and Preparing Data
Importing Data to Azure ML: us/documentation/articles/machine-learning-data- science-import-data/

18. Inspecting data Keys to quality source data Authority Spread
Consistency
Types and Units
Representation
1. In reference to machine learning, but applicable to all data usage: us/documentation/articles/machine-learning-data-science- prepare-data/

19. Azure Storage Types: Create with: Blobs Tables Queues Files
Azure Portal
Azure PowerShell
Azure Command Line Interface (CLI)
Service Management REST API
Azure Storage Resource Provider REST API
Azure Portal -
Azure PowerShell - us/documentation/articles/storage-powershell-guide-full/
AZCOPY - us/documentation/articles/storage-use-azcopy/
Azure CLI - us/documentation/articles/storage-azure-cli/
Service management REST API -
Azure Storage Resource Provider REST API -

20. Redundancy and Location
LRS: 3 Copies, 1 Datacenter
GRS: 6 Copies, 2 Datacenters
ZRS: 3 Copies, 2-3 Datacenters
Locations and Redundancy Overview: us/documentation/articles/storage-introduction/
Affects on Scalability and Performance Targets: us/documentation/articles/storage-scalability-targets/
Pricing Details: us/pricing/details/storage/

21. Tag the data descriptions Make it easy to find data in context
Azure Data Catalog
Register data sources
Tag the data descriptions
Make it easy to find data in context
Use the data – keep it secure
Full example: us/documentation/articles/data-catalog-get-started/

22. Options for Data Sourcing
Import from local
Import from Online
Import from Experiment
Getting data: us/documentation/articles/machine-learning-data-science- import-data/
Data Science Blog:

23. Algorithms

24. Clustering Grouping items based on defined Features
us/documentation/articles/machine-learning-algorithm- choice/
US/library/azure/dn aspx
us/library/azure/dn aspx
us/library/azure/dn aspx
us/library/azure/dn aspx

25. Classification
Predicting the class or category for a single instance of data
us/documentation/articles/machine-learning-algorithm- choice/
US/library/azure/dn aspx
us/library/azure/dn aspx
us/library/azure/dn aspx
us/library/azure/dn aspx

26. Anomaly Detection
Selecting items based on unusual or suspicious patterns
us/documentation/articles/machine-learning-algorithm- choice/
US/library/azure/dn aspx
us/library/azure/dn aspx
us/library/azure/dn aspx
us/library/azure/dn aspx

27. Regression Predicting the value of a datum given its history
us/documentation/articles/machine-learning-algorithm- choice/
US/library/azure/dn aspx
us/library/azure/dn aspx
us/library/azure/dn aspx
us/library/azure/dn aspx

28. Model Scoring and Evaluation
Train and Evaluate your Model: us/documentation/articles/machine-learning-walkthrough-4- train-and-evaluate-models/

29. Scoring a Model Apply a trained model to: A list of recommended items
Forecasts for time series models
Estimates of projected demand, volume, or other numeric quantity, for regression models
Cluster assignments
A predicted class or outcome, for classification models
Probability scores associated with these outputs
us/documentation/articles/machine-learning-algorithm- choice/
US/library/azure/dn aspx
us/library/azure/dn aspx
us/library/azure/dn aspx
us/library/azure/dn aspx

30. Evaluating a Model Metrics for Classification Models
Accuracy, Recall, Precision, F1-Score
AUC
Average Log Loss
Training Log Loss
Metrics for Regression Models
Mean absolute error (MAE)
Root mean squared error (RMSE)
Relative absolute error (RAE)
Relative squared error (RSE)
Coefficient of determination
Simple explanation of the ROC Curve: two-lines-of-code.html
us/library/azure/dn aspx
us/documentation/articles/machine-learning-evaluate- model-performance/
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31. Lab Create and Run an Azure ML Experiment
Open this site, and follow the steps listed there: us/documentation/articles/machine-learning-walkthrough- develop-predictive-solution/

32. Publishing and Using the Model

33. Options for Data Access
Azure ML API
Code
Push to Storage
Power BI / Excel
Access and read through this page: Microsoft-Azure-Blob-Storage-Power-Query-f8165faa b1-86b6-7015b330d13e?ui=en-US&rs=en- US&ad=US&fromAR=1
Access and read through this page: azure-and-sql-database-tutorials-tutorial-1-using- azure-web-role-and-azure-table-service.aspx
Accessing storage using Code: us/documentation/articles/storage-dotnet-how-to-use- blobs/
Working with Azure Storage: us/documentation/services/storage/
Data Science Blog:

34. © 2016 Microsoft Corporation. All rights reserved
© 2016 Microsoft Corporation. All rights reserved. Microsoft, Windows, Microsoft Azure, and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION