AWS/Machine Learning

This article will be about Amazon Web Services - Machine Learning (ML).

Machine Learning concepts

What is Machine Learning (ML)?

• The basic concept of ML is to have computers or machines program themselves.
• Machines can analyze large and complex datasets and identify patterns to create models, which are then used to predict outcomes.
• Over time, these models can take into account new datasets and improve the accuracy of the predictions.

Examples of where ML is being used

• Recommendations when checking out on an e-commerce site (e.g., purchases on Amazon.com)
• Spam detection in email
• Any kind of image, speech, or text recognition
• Weather forecasts
• Search engines

What is Amazon ML?

• Amazon ML is supervised ML; learns from examples or historical data.
• An Amazon ML Model requires your dataset to have both the features and the target for each observation/record.
• A feature is an attribute of a record used to identify patterns; typically, there will be multiple features.
• A target is the outcome that the patterns are linked to and is the value the ML algorithm is going to predict.
• This linking is used to predict the outcomes
• Example: {Go to the grocery store} {on Monday} (attribute {feature}) => Buy milk (target)

Why do ML on AWS?

• Simplifies the whole process
• No coding required for creating models
• Identifies the best ML algorithm to run based on the input data
• Easily integrates into other AWS services for data retrieval
• Deploy within minutes
• Full access via APIs
• Scalable

Amazon ML pricing (as of March 2017)

• Data Analysis and Model Building fees: $0.42/hour
• Batch Predictions $0.10 per 1,000 predictions, rounded up to the next 1,000
• Real-time predictions: $0.0001 per prediction, rounded up to the nearest penny (plus hourly capacity reservation charge only when the endpoint is active)

AWS ML Workflow

1. Create a data source
   • S3 (i.e., upload a CSV file to S3)
   • RDS and Redshift (i.e., run a SQL query on a Redshift cluster and get the data back directly into ML)
2. Identify the feature and target columns
   • Select whether the file has a header row
   • Select the correct field data types (possible types: binary, categorical, numeric, text)
   • Select the target that needs to be predicted
   • Select a Row ID, if the data has one
3. Train a model with a part of the dataset (generally 70%)
   • By default, AWS ML takes 70% of your data and uses it to train the model
   • It also automatically decides the best ML Model algorithm to use, based on the data schema
     • Binary target => binary model
     • Numeric target => regression model
     • Categorical target => multi-class model
4. Evaluate the model by running the remaining dataset through it
   • AWS ML automatically evaluates the model based on the data source for you
   • If using the API, you would have to do this in a separate step
5. Fine-tune the model
6. Use the model for predictions

Types of ML models available

• Binary
  • The target/prediction value is a 0 or 1
  • Best used when the prediction is a Boolean or one of two possible outcomes (e.g., true/false, yes/no, green apple/red apple, etc.)
  • Examples:
    • Does an email match the spam criteria?
    • Will someone respond to a marketing email?
    • Does a purchase on a credit card seem fraudulent?
• Multi-class
  • The target/prediction is from a set of values
  • Best used for predicting categories or types
  • Examples:
    • What is the next product a user will purchase based on his/her history of purchases?
    • Film recommendations
• Regression
  • The target/prediction is a numeric value
  • Best used for predicting scores
  • Root mean square error (RMSE)
    • AWS ML takes the mean of the training target data (RMSE Baseline) and uses that as a baseline and compares it to the mean of the predictions (RMSE)
    • A RMSE lower than the RMSE Baseline is better
  • Examples:
    • How many millimetres of rain can we expect?
    • Traffic delays
    • How many goals will my soccer team score?

External links

• AWS Machine Learning