Choose your scenario and prepare data


In this unit, you'll learn about choosing a scenario, selecting a training environment, and preparing your data for training in Model Builder.

Start the training process

To start the training process, you need to add a new Machine Learning Model (ML.NET) item to a new or existing .NET application.


Portability is one of the benefits that class libraries give you. Class libraries make it easy to reference any models that are part of it from the console, desktop, web, and any other type of .NET application. We recommend that you add Machine Learning Model (ML.NET) items to a class library.

The Machine Learning Model (ML.NET) item adds a file to your project with the .mbconfig file extension. Files that use the .mbconfig extension are Model Builder configuration files authored in JSON. These files allow you to:

  • Provide a name for your model.
  • Collaborate with others on your team via source control.
  • Persist state. If at any point in the training process you need to close Model Builder, your state is saved and you can pick up where you left off.

Choose a scenario

The first step to train a machine learning model is deciding which scenario and machine learning task are the most appropriate, given what you're trying to predict.

What is a scenario?

A scenario describes the problem you're trying to solve by using your data. Common scenarios include the ones listed in the previous unit:

  • Categorizing data: Organize news articles by topic.
  • Predicting a numerical value: Estimate the price of a home.
  • Grouping items with similar characteristics: Segment customers.
  • Classifying images: Tag an image based on its contents.
  • Recommending items: Recommend movies.
  • Detecting objects in an image: Detect pedestrians and bicycles at an intersection.

The scenarios map to machine learning tasks. A machine learning task is the type of prediction or inference being made, based on the problem or question that's being asked and the available data.

Machine learning tasks tend to fall into two categories:

  • Supervised
  • Unsupervised

The main difference between them is whether the label, or the value that you're trying to predict, is known or not.

For supervised tasks, the label is known. Examples of supervised machine learning tasks include:

  • Classification
    • Binary (two categories)
    • Multiclass (two or more categories)
    • Image
  • Regression

For unsupervised tasks, the label is unknown. Examples of unsupervised machine learning tasks include:

  • Clustering
  • Anomaly detection

Supported scenarios in Model Builder

Model Builder supports the following scenarios that map to machine learning tasks:

Scenario Machine learning task Use case
Data classification Binary and multiclass classification Organize articles by topic.
Value prediction Linear regression Predict the price of a home based on features of the home.
Image classification Image classification (deep learning) Organize images by animal species based on the content of an image.
Recommendation Recommendation Recommend movies based on the preferences of similar users.
Object detection Object detection (deep learning) Identify physical damage in an image.

Choosing a scenario for predictive maintenance

Depending on what your data looks like, the predictive maintenance problem can be modeled through different tasks. For your use case, because the label is a binary value (0 or 1) that describes whether a machine is broken or not, the data classification scenario is appropriate.

Choose your environment

Now that you've selected the data classification, it's time to choose the environment where you want to train your machine learning model. Environments describe the compute resources that you'll use to train your machine learning model.

Supported environments in Model Builder

Model Builder supports the following environment options:

Scenario Local CPU Local GPU Azure GPU
Data classification ✔️
Value prediction ✔️
Image classification ✔️ ✔️ ✔️
Recommendation ✔️
Object detection ✔️

Depending on your use case, there are various reasons why you might choose a local or Azure environment.

Local environments

Reasons why you might consider local compute environments include:

  • Training locally doesn't cost you anything because you're using your computer's resources.
  • You don't want your data to leave your computer or datacenter.

Azure environments

Scenarios like image classification and object detection are resource intensive. Using a GPU can often speed up the training process. If you don't have a GPU or a computer with enough CPU or RAM, offloading the training process to Azure can lighten the load on your system.

Load and prepare your data

With your scenario and training environment selected, it's time to load and prepare your data.

Data is the most important component for building machine learning models. The process for loading data into Model Builder consists of three steps:

  1. Choose your data source type.
  2. Provide the location of your data.
  3. Choose column purpose.

Choose your data source type

Depending on your scenario, Model Builder supports loading data from the following sources:

  • Delimited files (comma, semicolon, and tab)
  • Local and remote SQL Server databases
  • Images (.jpg and .png)
  • Visual Object Tagging Tool (VOTT) for object detection scenarios

Provide the location of your data

After you've selected your data source type, you have to provide the location where your dataset is stored. This location can be a directory, file path, or database connection string. It's dependent on the scenario and data source type that you've selected.

When a data source is selected in Model Builder, it parses the data and makes its best effort to identify:

  • Headers and column names
  • Column separator
  • Column data types
  • Column purpose
  • Decimal separators

After the data is loaded, Model Builder displays a preview of some of the elements in your dataset.

Choose column purpose

Depending on the scenario that you choose, you'll have to define the purpose of certain columns. In scenarios like data classification and value prediction, you'll have to choose which of your columns is the column that you want to predict (label).

By default, all other columns that are not the label are used as features. Features are columns used as inputs to predict the label.

Advanced data options

To customize how your data is loaded, Model Builder provides advanced data options. These options allow you to customize settings that pertain to your columns and dataset formatting.

For columns, you can choose the following settings:

  • Purpose: Should the column be a feature, be a label, or be ignored? You can have only one column selected as the label.
  • Data type: Is the value a single-precision float value, string, or Boolean?
  • Categorical: Does the column represent a categorical value (for example: low, medium, or high)?

To format data, you can choose whether the data contains column headers, the column separator (comma, semicolon, or tab), and the decimal separator type (period or comma).

Understanding the predictive maintenance dataset

The dataset used in this module is the AI4I 2020 Predictive Maintenance Dataset. This synthetic dataset reflects real predictive maintenance data encountered in industry. It consists of 10,000 data points and 14 columns. It's from:

Explainable Artificial Intelligence for Predictive Maintenance Applications, Stephan Matzka, Third International Conference on Artificial Intelligence for Industries (AI4I 2020), 2020 (in press), and hosted at the UCI Machine Learning Repository - Dua, D. and Graff, C. (2019). UCI Machine Learning Repository Irvine, CA: University of California, School of Information and Computer Science.

Here's a preview of what the data looks like:

UDI Product ID Type Air temperature [K] Process temperature [K] Rotational speed [rpm] Torque [Nm] Tool wear [min] Machine failure TWF HDF PWF OSF RNF
1 M14860 M 298.1 308.6 1551 42.8 0 0 0 0 0 0 0
162 L47341 L 298.3 308.1 1412 52.3 218 1 0 0 0 1 0

The columns are defined as follows:

  • UDI: The row's index.
  • Product ID: A product identifier that contains a product type category and a variant-specific serial number.
  • Type: Product quality category. The values are L (low; 50% of all products), M (medium; 30%), or H (high; 20%).
  • Air temperature [K], Process temperature [K], Rotational speed [rpm], Torque [Nm], Tool wear [min]: Values collected from sensors.
  • Machine failure: Binary label (0 or 1) that indicates whether the machine has failed.
  • TWF, HDF, PWF, OSF, RNF: Independent machine failure modes. A value of 1 indicates that the respective failure mode occurred.

For this scenario, you won't use all of the columns in the dataset because they either don't inform the prediction or contain redundant information.

Because you want to be able to predict whether a machine will fail or not, the Machine failure column is the label. In Model Builder, for features, you can use data from the Product ID, Type, and various sensor columns.

Although the failure modes are useful in diagnosing the root cause of a failure, they aren't useful for your use case. The reason is that you only want to know whether a machine has failed or not. Additionally, similar information is already captured by the Machine failure column. So, you can ignore these columns.

Proceed to the next unit to check your knowledge on scenarios, environments, and data preparation.