1. An Intro to AWS Machine Learning
PREDICTIVE ANALYTICS
An Intro to AWS Machine Learning
AGENDA
About me
Predictive Analytics
Amazon Machine Learning (ML)
Amazon ML – Key Concepts
Amazon ML – Datasources
Amazon ML – Models
Amazon ML – Evaluations
Amazon ML – Demo

2. ABOUT Me
Naveen VK
Principal Architect at NVISIA, a regional software development company
Worked for NVISIA for over 17 years
Designed and built custom multi-tier applications using Java Enterprise stack for various companies
Involved in entire application development lifecycle including requirements gathering, architecture, design, implementation, integration, testing and deployment
Some clients: ETF - State of WI, American Family, Harley Davidson, Cumulus Media
Currently working at ETF (Employee Trust Fund)
Manage pensions, insurance and other benefits for state and local employees
Involved in multiple projects (5) and currently supporting multiple applications (7)
Has deep expertise in databases like Oracle (since 1994) and DB2 (since 1999) and with SQL queries and PL/SQL stored procedures
3 fun facts about myself
NVISIA® Confidential 2016

3. Predictive Analytics
What is Predictive Analytics? Some use cases/examples
NVISIA® Confidential 2016

4. PREDICTIVe ANALYTICS What is it?
Mining data, using statistical algorithms and machine learning to predict trends or probabilities
Use historical data and patterns in historical data to predict future
Create models based on patterns in data to predict the probability of something happening in the future
The better the model and the training data, the better the prediction
Examples
Is this spam?
Will this product sell?
How many units of this product will sell?
Is this product a piece of clothing, a book or a movie?
What price will this house sell for?
What will be the temperature here tomorrow?
NVISIA® Confidential 2016

5. Amazon machine learning (ml)
What is it? When to use it?
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6. Amazon machine learning (mL)
AWS (Amazon Web Service) cloud-based service for predictive analytics
Use tools and wizards to create machine learning models
Use simple APIs to obtain predictions for your application
No need to write custom code or have supporting infrastructure
Finds patterns in your existing data
Use models to process new data and generate predictions
When to use ML?
ML is not a solution for every type of problem
A target value can be determined by coding simple rules, computations and steps without any data-driven learning
Use ML when the rules cannot be programmed easily
Too many factors
Too many overlapping rules
Too much fine tuning of rules
Use ML when the solution cannot be scaled
100s of Millions vs. 100s (Example: manual vs. automated spam filter)
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7. Amazon ml – Key Concepts
Terms and concepts
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8. Amazon mL – Key concepts
Datasources
Contains metadata associated with data inputs to the ML
Speadsheets, CSV files, Streaming data, Relational data base
ML Models
Patterns in data to generate predictions
Evaluations
Measure the quality of ML models
Batch Predictions
Multiple data inputs aka batch data
Asynchronous
Realtime Predictions
Individual data inputs
Synchronous
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9. Amazon ml – Datasources
Details of datasources in Amazon ML
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10. Amazon mL – Datasources
In Amazon ML, a datasource contains only the metadata about the actual input data
Actual data may be stored in
Amazon S3 buckets
Amazon Redshift Databases
MySQL databases in Amazon Relational Database Service (RDS)
Amazon Kinesis
Attributes
Column headings represent attributes
Unique
Required
Target Attribute
The data that is being predicted
Training data has a target attribute that has already been predicted (required in training data)
Observation
Single row of data
Input data
All observations aka Rows in spreadsheet/csv file or database
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11. Amazon mL – Datasources continued
Schema
All attributes and corresponding data-types of input data
Location
Location of input data stored in, say, Amazon S3 bucket
Row ID
Attribute flagged to be included in prediction output
Helps cross-reference the prediction with the observation
Unique for each observation
Optional
Datasource Name
Human readable name of the datasource
Statistics
Summary stats for each attribute of input data
Status
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12. Amazon ml – MODEL Details of mathematical model in Amazon ML
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13. Amazon mL – MODEL
In Amazon ML, a model finds patterns in data and generates predictions
Three distinct types of models
Binary
Multiclass
Regression
Type of model chosen based on the type of target to predict
Binary Model
Predicts values that has 1 of 2 states: true/false, 1/0, win/lose, alive/dead, pass/fail, healthy/sick
Uses industry-wide standard learning algorithm called Binary Logistic Regression Algorithm
Statistical model used to predict the probability of a binary response based on certain variables
Examples
Is this spam?
Will this product sell?
Multiclass Model
Predicts values that belong to a pre-defined, limited set of states (1 of 3 or more states)
Uses industry-wide standard learning algorithm called Multinomial Logistic Regression Algorithm
Is this product a book, a movie or apparel?
Is this movie a thriller, a documentary or a comedy?
NVISIA® Confidential 2016

14. Amazon mL – MODEL Regression Model Predicts a numeric value
For regression problems
Uses industry-wide standard learning algorithm called Linear Regression Algorithm
Statistical model to predict the value of y based on a number of variables x1, x2, x3, etc.
Examples:
What will the temperature be tomorrow?
How many units of this product will sell?
How much will this house sell for?
Recipe
Attributes and attribute transformations available to train the model
Model size
In MB
Directly proportional to patterns stored in model
Number of passes
The number of times the datasource is used when training the model
Regularization
ML technique to get higher quality models
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15. Amazon ml – Evaluations
Evaluate the model in Amazon ML
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16. Amazon mL – EVALUATIONS
In Amazon ML, an evaluation measures the quality of the ML model
Need to evaluate a model to determine if it will do a good job predicting the target on new/future data
Need training data where target is already predicted to train/evaluate a model
Max size of training data: 100KB
Model Insight
Amazon ML will provide metrics and insights to review accuracy of the model
Overall success metric of the model
Visualizations to explore accuracy of model
Alerts to check validity of evaluation
Focus on Binary Insights only for this presentation
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17. Amazon mL – EVALUATIONS – Binary insights
Prediction score
Actual output of the binary prediction
Indicates the system’s certainty that the given observation has target value of 1
Output scores of observations is between 0 & 1
Default threshold score aka cut-off is 0.5, this can be changed
Any observation that scores above cut-off is predicted as target=1 and below cut-off is predicted as 0
Correct predictions
True Positive (TP)
Predicted value of target = 1, true value of target = 1
True Negative (TN)
Predicted value of target = 0, true value of target = 0
Incorrect predictions
False Positive (FP)
Predicted value of target = 1, true value of target = 0
False Negative (FN)
Predicted value of target = 0, true value of target = 1
Area Under the Curve (AUC)
Measures the ability of the model to make a correct prediction
AUC near 1 indicates model is highly accurate (near 0s?)
NVISIA® Confidential 2016

18. Amazon mL – EVALUATIONS – Binary insights – AUC (AWS Tutorial)
NVISIA® Confidential 2016

19. Amazon mL – DEMO – Binary model
Simple – predicting will this product sell?
Not so simple – predicting will this person survive?
Checklist
Predictive Analytics
Amazon Machine Learning (ML)
Amazon ML – Key Concepts
Amazon ML – Datasources
Amazon ML – Models
Amazon ML – Evaluations
Amazon ML – Demo
Pricing
Data analysis and model
Batch predictions: $0.10/nearest 1000 (rounded up to the next 1000)
Realtime predictions: $0.0001/transaction (rounded to nearest penny)
S3 Standard storage: $0.03/TB/month
Questions
NVISIA® Confidential 2016

20. Thank YOU For COMING
Links: Contact Info: Linked-In: Naveen VK (work) (personal) Github: