Amazon Machine Learning (Amazon ML) is a robust, cloud-based service that makes it easy for developers of all skill levels to use machine learning technology. Amazon ML provides visualization tools and wizards that guide you through the process of creating machine learning (ML) models without having to learn complex ML algorithms and technology. Once your models are ready, Amazon ML makes it easy to obtain predictions for your application using simple APIs, without having to implement custom prediction generation code or manage any infrastructure.This section introduces the key concepts and terms that will help you understand what you need to do to create powerful machine learning models with Amazon ML.
- Amazon Machine Learning Key Concepts
- Accessing Amazon Machine Learning
- Regions and Endpoints
- Pricing for Amazon ML
Amazon Machine Learning Key Concepts
This section summarizes the following key concepts and describes in greater detail how they are used within Amazon ML:
- Datasources contain metadata associated with data inputs to Amazon ML
- ML Models generate predictions using the patterns extracted from the input data
- EvaluationsEvaluations measure the quality of ML models
- Batch PredictionsBatch Predictions asynchronously generate predictions for multiple input data observations
- Real-time Predictions synchronously generate predictions for individual data observations
A datasource is an object that contains metadata about your input data. Amazon ML reads your input data, computes descriptive statistics on its attributes, and stores the statistics—along with a schema and other information—as part of the datasource object. Next, Amazon ML uses the datasource to train and evaluate an ML model and generate batch predictions.
A datasource does not store a copy of your input data. Instead, it stores a reference to the Amazon S3 location where your input data resides. If you move or change the Amazon S3 file, Amazon ML cannot access or use it to create an ML model, generate evaluations, or generate predictions.
The following table defines terms that are related to datasources.
|Attribute||A unique, named property within an observation. In tabular-formatted data such as spreadsheets or comma-separated values (CSV) files, the column headings represent the attributes, and the rows contain values for each attribute.|
Synonyms: variable, variable name, field, column
|Datasource Name||(Optional) Allows you to define a human-readable name for a datasource. These names enable you to find and manage your datasources in the Amazon ML console.|
|Input Data||Collective name for all the observations that are referred to by a data source.|
|Location||of input data. Currently, Amazon ML can use data that is stored in Amazon S3 buckets, Amazon Redshift databases, or MySQL databases in Amazon Relational Database Service (RDS).|
|Observation||A single input data unit. For example, if you are creating an ML model to detect fraudulent transactions, your input data will consist of many observations, each representing an individual transaction.|
Synonyms: record, example, instance, row
|Row ID||(Optional) A flag that, if specified, identifies an attribute in the input data to be included in the prediction output. This attribute makes it easier to associate which prediction corresponds to which observation.|
Synonyms: row identifier
|Schema||The information needed to interpret the input data, including attribute names and their assigned data types, and names of special attributes.|
|Statistics||Summary statistics for each attribute in the input data. These statistics serve two purposes:|
The Amazon ML console displays them in graphs to help you understand your data at-a-glance and identify irregularities or errors.
Amazon ML uses them during the training process to improve the quality of the resulting ML model.
|Status||Indicates the current state of the data source, such as In Progress, Completed, or Failed.|
|Target Attribute||In the context of training an ML model, the target attribute identifies the name of the attribute in the input data that contains the “correct” answers. Amazon ML uses this to discover patterns in the input data and generates an ML model. In the context of evaluating and generating predictions, the target attribute is the attribute whose value will be predicted by a trained ML model.|
An ML model is a mathematical model that generates predictions by finding patterns in your data. Amazon ML supports three types of ML models: binary classification, multiclass classification, and regression.
The following table defines terms that are related to ML models.
|Regression||The goal of training a regression ML model is to predict a numeric value.|
|Multiclass||The goal of training a multiclass ML model is to predict values that belong to a limited, pre-defined set of permissible values.|
|Binary||The goal of training a binary ML model is to predict values that can only have one of two states, such as true or false.|
|Model Size||ML models capture and store patterns. The more patterns a ML model stores, the bigger it will be. ML model size is described in Mbytes.|
|Number of Passes||When you train an ML model, you use data from a datasource. It is sometimes beneficial to use each data record in the learning process more than once. The number of times that you let Amazon ML use the same data records is called the number of passes.|
|Regularization||Regularization is a machine learning technique that you can use to obtain higher-quality models. Amazon ML offers a default setting that works well for most cases.|
An evaluation measures the quality of your ML model and determines if it is performing well.
The following table defines terms that are related to evaluations.
|Model Insights||Amazon ML provides you with a metric and a number of insights that you can use to evaluate the predictive performance of your model.|
|AUC||Area Under the ROC Curve (AUC) measures the ability of a binary ML model to predict a higher score for positive examples as compared to negative examples.|
|Macro-averaged F1-score||The macro-averaged F1-score is used to evaluate the predictive performance of multiclass ML models.|
|RMSE||The Root Mean Square Error (RMSE) is a metric used to evaluate the predictive performance of regression ML models.|
|Cut-off||ML models work by generating numeric prediction scores. By applying a cut-off value, the system converts these scores into 0 and 1 labels.|
|Accuracy||Accuracy measures the percentage of correct predictions.|
|Precision||Precision shows the percentage of actual positive instances (as opposed to false positives) among those instances that have been retrieved (those predicted to be positive). In other words, how many selected items are positive?|
|Recall||Recall shows the percentage of actual positives among the total number of relevant instances (actual positives). In other words, how many positive items are selected?|
Batch predictions are for a set of observations that can run all at once. This is ideal for predictive analyses that do not have a real-time requirement.
The following table defines terms that are related to batch predictions.
|Output Location||The results of a batch prediction are stored in an S3 bucket output location.|
|Manifest File||This file relates each input data file with its associated batch prediction results. It is stored in the S3 bucket output location.|
Real-time predictions are for applications with a low latency requirement, such as interactive web, mobile, or desktop applications. Any ML model can be queried for predictions by using the low latency real-time prediction API.
The following table defines terms that are related to real-time predictions.
|Real-time Prediction API||The Real-time Prediction API accepts a single input observation in the request payload and returns the prediction in the response.|
|Real-time Prediction Endpoint||To use an ML model with the real-time prediction API, you need to create a real-time prediction endpoint. Once created, the endpoint contains the URL that you can use to request real-time predictions.|