AWS : ML (Machine Learning)

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AWS Machine Learning (ML)

The AWS Management Console provides a web-based interface for creating, accessing, and managing all Amazon Machine Learning entities.

The Console also includes a walk-through tutorial that guides us through the process of building a machine learning model.

Programmatic access to Amazon Machine Learning is enabled by the AWS SDKs and the AWS Mobile SDK.

We can also create and manage Amazon Machine Learning entities using the AWS Command Line Interface (CLI) available on Windows, Mac, and Linux/UNIX systems.

Get started

Click Launch.

Input Data

Type in bogo-devops/banking.csv for S3 location.

Click "Verify":

In the S3 permissions dialog box, choose Yes.

If Amazon ML can access and read the data file at the S3 location, we will see a page similar to the following.

Review the properties, and then choose Continue.

After we upload the banking.csv dataset to Amazon Simple Storage Service (Amazon S3) location, we will use it to create a training datasource.

A datasource is an Amazon Machine Learning (Amazon ML) object that contains the location of our input data and important metadata about our input data. Amazon ML uses the datasource for operations like ML model training and evaluation.

Schema

Next, we need to establish a schema.

A schema is the information Amazon ML needs to interpret the input data for an ML model, including attribute names and their assigned data types, and the names of special attributes.

In this tutorial, we'll ask Amazon ML to infer the attribute types and create a schema for us.

Amazon ML has correctly identified the data types for all of the attributes, so choose Continue.

Target

Next, select a target attribute.

The target is the attribute that the ML model must learn to predict.

Attribute y indicates whether an individual has subscribed to a campaign in the past: 1 (yes) or 0 (no).

Row ID

On the Row ID page, for Does your data contain an identifier? , make sure that No, the default, is selected.

Review

Choose Continue.

Create an ML Model

Now that we've created the training datasource, we can use it to create an ML model, train the model, and then evaluate the results.

The ML model is a collection of patterns that Amazon ML finds in our data during training.

We use the model to create predictions.

Because the Get started wizard creates both a training datasource and a model, Amazon Machine Learning (Amazon ML) automatically uses the training datasource that weust created, and takes us directly to the ML model settings page.

On the ML model settings page, for ML model name, make sure that the default, ML model: Banking Data 1, is displayed.

Choose Review, review the settings, and then choose Finish.

After we choose Finish, Amazon ML adds our model to the processing queue.

When Amazon ML creates our model, it applies the defaults and performs the following actions:

Splits the training datasource into two sections, one containing 70% of the data and one containing the remaining 30%.
Trains the ML model on the section that contains 70% of the input data.
Evaluates the model using the remaining 30% of the input data.

Now we are ready to review our models performance and set a cut-off score.

ML Model's Predictive Performance

Ref : Review the ML Model's Predictive Performance and Set a Score Threshold.

Now that we've created our ML model and Amazon Machine Learning (Amazon ML) has evaluated it, let's see if it is good enough to put to use.