1. Amazon Machine Learning
Brody Dumas, Daniel Adams, Joe Bernardi, Matt Siegel

2. Agenda What is Machine Learning? Product Overview Key Features Pricing
Use Cases
Pros and Cons
Demo
Questions
Good Morning everyone, my name is Brody Dumas…… We’re going to talk about Amazon Machine Learning today.
So today we’re going to start off by understanding what exactly machine learning is. We’re then going to go into depth about Amazon’s version of Machine Learning. After that, Daniel will highlight the key features of Amazon’s service. Then Joe will go over pricing and show everyone an example of their pricing model. He’ll also cover some real business use cases in which the machine learning service is used by companies. Finally, Matt will discuss the pros and cons and will show everyone a quick demo of the service. We’ll wrap up with a few minutes for questions.

3. What is Machine Learning?
"Field of study that gives computers the ability to learn without being explicitly programmed" - Arthur Samuel, 1959
Explores the construction of algorithms that can learn from and make predictions on data
These algorithms build models from example inputs to make better informed, data-driven predictions and decisions
So what is machine learning? Well, Arthur Samuel, a pioneer in the artificial intelligence field had this to say about machine learning. He said, “”. And although we’ve come a long way from Samuel’s self-learning checkers game, the basis of machine learning still rings true in his statement. Now, ML explores the …… And these algorithms then build…….
Source:
world's first self-learning program

4. Product Overview Cloud-based Amazon Web Service (AWS)
Enables easy modeling with Machine Learning technology
Provides Application Program Interfaces (APIs) for developers to make their programs smarter
Released publicly in 2015, but has been used in-house by Amazon’s data scientist previously
So how does Amazon implement their own machine learning service? Well, like many of Amazon’s web services, ML is a cloud-based system. Their service enable easy-to-use modeling for simple business uses while also providing APIs for developers to make their programs smarter. And although it was only released publicly in 2015, Amazon has been using this service internally for years. So now Daniel is going to go more in depth with what Amazon’s ML Key Features are.

5. Key Features

6. Pricing Data Analysis and Model Building Fees: $0.42 per hour
Prediction Fees:
Batch Predictions: $0.10 per 1,000 predictions, rounded up to the next 1,000
Real-Time Predictions: $ per prediction, rounded up to the nearest penny
Other Pricing Details: Data stored separately in other AWS services are billed separately
Source:

7. Pricing Example (Batch)
Compute Fees
The compute price is $0.42 per hour.
Total compute fees = Compute time (hrs) * $0.42
Total compute fees = 20 hrs * $0.42/hr = $8.40
Monthly Prediction Fees
The monthly price for batch predictions is $0.10 per predictions
Total prediction fees = ($0.10/1000) * 900,000 = $90.00
Total Monthly Fees
Compute Fees + Prediction Fees = $ $90.00 = $98.40
Source:

8. Use Cases Fraud Protection Churn Rate Prediction Sentiment Analysis
Business Improvement
Content Personalization

9. Pros and Cons Pros: Faster Predictions than Google Prediction
Most accurate compared to direct competitors
Real-time and Batch Predictions
Everything done on Amazon Cloud
Cons:
Models can’t be exported or imported -- all work tied to Amazon
Limited dataset size
Restrictive algorithms
Compared to Google Prediction, PredicSis, and BigML:
Slowest in training
3rd slowest in predictions

10. Questions?