Amazon Machine Learning| ML Key Concepts
Tags: machinelearning, aws, datascience
Published: 2021-11-24 14:56:34


# Amazon Machine Learning Key Concepts

**Data sources**

|**Term**|**Definition**|
|---|---|
|Attribute|A unique, named property within an observation. In tabular-formatted data such as spreadsheets or CSV files|
|Datasource Name|A unique name for a dataset|
|Input Data|Collective name for all the observations that are referred to by a datasource.|
|Location|Amazon ML can use data that is stored within Amazon S3 buckets, Amazon Redshift databases, or MySQL databases in Amazon Relational Database Service (RDS)|
|Observation|A single data point that is part of a datasource|
|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|
|Status	|Indicates the current state of the datasource, such as In Progress, Completed, or Failed.|
|Target Attribute|the target attribute is the attribute whose value will be predicted by a trained ML model|

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## ML Models


|**Term**|**Definition**|
|---|---|
|Regression|ML model to predict a numeric value|
|Multiclass|ML model to predict values that belong to a limited, pre-defined set of permissible values.|
|Binary|ML model to predict values that can only have one of two state|
|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|he 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|
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## Evaluations


|**Term**|**Definition**|
|---|---|
|Model Insights|Amazon ML provides you with a metric to evaluate the predictive performance of your model.|
|Precision|the number of positive class predictions that actually belong to the positive class.|
|Recall | the number of positive class predictions made out of all positive examples in the dataset.|
|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|
|Accuracy|Accuracy measures the percentage of correct predictions.|
|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|The cut-off is the threshold that you use to determine whether a predicted value is correct or not.|


![](https://i.pinimg.com/736x/7d/49/d5/7d49d532ebbdd5247f121adfbe77b688.jpg)

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## Batch Predictions


|**Term**|**Definition**|
|---|---|
|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.|

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## Real-time Predictions

Real-time predictions are for applications with a low latency requirement, such as interactive web, mobile, or desktop applications.

|**Term**|**Definition**|
|---|---|
|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.|

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