This process involves the definition, implementation, and management of models that are tailored to meet specific industry- and organization-specific needs.
Training Custom Models Using Power Apps
The initial steps of training custom models in Power Apps involve setting up and preparing the necessary elements.
Data Preparation
Before you commence model training, you need to prepare your data. Power Apps supports data input through different sources such as Excel data, SharePoint, and Common Data Service (CDS). It is recommended to use data that is cleaned and well-structured to ensure the quality and effectiveness of the model training.
Model Creation
After preparing your data, create the model. Navigate to Power Apps on your desktop and use the AI Builder to create the model. This feature allows you to build, train, and publish AI models that align with your organization’s unique scenarios.
To create a model, follow these steps:
- Navigate to the AI Builder section and click on the “Build” option.
- Under the Model types, select “Custom model” and follow the guided process.
- Specify the field(s) to be predicted and then select the fields that the model should use to make that prediction.
- Name your model and then click Create to build your model.
Model Training and Evaluation
After preparing your data and model, the next step is to train the model. Model training is an automated process in Power Apps. This process involves teaching your model the patterns and relationships between different elements in your database to make accurate predictions.
To train your model, go to Models in AI Builder, select your custom model, and click on the Train button. Training could take a few minutes depending on the data size.
Evaluation of the model follows training. This stage lets you understand the effectiveness of the model and how well it can make predictions. It provides summarized information about the performance like Accuracy, Recall, Precision, and F1 Score.
Model Publishing
Publishing is the final step in training a custom model. This step makes your model available for prediction or further application across your business platform.
To publish a model, select the model, then click the Publish button.
Model Types in Power Apps
The AI Builder in Power Apps provides a multitude of model types to suit diverse prediction requirements:
- Categorization models: These are models that categorize text based on learned linguistic patterns.
- Binary prediction models: These models, as the name indicates, make binary decisions, predicting one outcome or the other.
- Form Processing models: These are designed to recognize predetermined types of forms amongst your documents.
- Object detection models: These models are designed to identify and locate objects from an image based on what it has learned from the training data.
It’s important to understand that the type of model selected will depend largely on the specific use case or business problem you’re trying to solve. Choosing the correct model impacts the overall performance of your solution.
In conclusion, understanding the process of training custom models is critical for anyone working with Power Apps or preparing for the PL-100 Microsoft Power Platform App Maker exam. Training a custom model involves preparing your data, creating the model, training and evaluation, and finally publishing the tailored model for prediction. By effectively understanding and implementing these steps, you can leverage the full potential of AI in Power Apps for your organization.
Practice Test
True or False: The process of training a custom model involves uploading data, training the model, and validating the model’s accuracy.
- True
- False
Answer: True
Explanation: The process for training custom models typically involves uploading data, training the model using that data and then validating the model’s accuracy before deploying for use.
What is the initial step in training a custom model?
- a) Testing the model
- b) Training the model
- c) Deploying the model
- d) Uploading data
Answer: d) Uploading data
Explanation: The initial step in training a custom model is uploading data because the model requires this data to learn and make accurate predictions.
Multiple Select: Which of the following are stages in training a custom model?
- a) Scaling the model
- b) Tuning the model
- c) Getting the model certified
- d) Assessing model performance
Answer: b) Tuning the model and d) Assessing model performance
Explanation: Tuning the model to refine its predictive capabilities and assessing its performance are key stages in the model training process. Scaling the model and getting it certified are not typically considered part of the training process.
True or False: Model training does not require the adjustment of its parameters or hyperparameters.
- True
- False
Answer: False
Explanation: Model training often involves tuning or adjusting the model’s hyperparameters to improve its predictive performance.
In Microsoft Power Platform, a new model version is created every time the model is _________.
- a) trained
- b) deployed
- c) tuned
- d) tested
Answer: a) trained
Explanation: In the Microsoft Power Platform, each time a model is trained, a new version is created. This allows for comparisons between different versions and the ability to select the most effective one.
In Microsoft Power Platform, which stage comes right after creating or selecting a model?
- a) Deploying the model
- b) Tuning the model
- c) Training the model
- d) Validating the model
Answer: c) Training the model
Explanation: In the Power Platform, once a model is created or selected, the next stage is to train the model using historical data.
True or False: After training a model, you need to manually create a new version of it in Microsoft Power Platform.
- True
- False
Answer: False
Explanation: In Microsoft Power Platform, every time you train a model, a new version is created automatically, alleviating the need for manual version creation.
Multiple Select: What are the possible ways to validate a model’s accuracy in Power Platform?
- a) Utilizing test data
- b) Comparing with previous model versions
- c) Using historical data
- d) Running an application
Answer: a) Utilizing test data and b) Comparing with previous model versions
Explanation: Validation of a model can be done by utilizing test data and comparing the performance with previous versions of the model. Using historical data is typically part of the training process, and running an application may not necessarily validate the model’s accuracy.
True or False: The training data used should not contain the target field.
- True
- False
Answer: False
Explanation: The training data used should indeed contain the target field because the model requires this information to learn and make accurate predictions.
What does it mean when a model is deemed ‘overfitting’?
- a) The model performs poorly in the training phase
- b) The model performs well in the training phase but poorly with new, unseen data
- c) The model performs well both in the training phase and with new, unseen data
- d) The model experiences performance decay over time
Answer: b) The model performs well in the training phase but poorly with new, unseen data
Explanation: Overfitting is a common problem in machine learning where a model performs well on the training data during the learning process, but does not generalize well to new, unseen data. This typically as a result of the model being too complex and capturing the noise in the training data.
Interview Questions
What is the first step in training custom models in Microsoft Power Platform App Maker?
The first step is preparing and importing the data that you plan to use for training the model.
What kind of data format is required to train a custom model?
Data required to train a custom model should be in .csv, .tsv, .excel format or a SharePoint list.
Is it necessary to clean and pre-process the data before using it for training the model?
Yes, it is very important to clean and preprocess the data to ensure the quality and relevancy of the data used for training.
What is the purpose of the ‘Train’ button in the Power Platform App Maker?
The ‘Train’ button is used to initiate the training of your model with the imported and processed data.
What happens during the training phase of a custom model?
During the training phase, the system uses machine learning techniques to learn and create the model.
After training the model, what is the next step in creating a custom model on the Power Platform App Maker?
The next step after training is to ‘Publish’ the trained model, making it ready for use.
What role does labelling data play in training a model?
Labelling data helps the model to understand which outcomes are correct in given situations, which aids successful training.
How can the accuracy of the trained model be evaluated?
The accuracy of the trained model can be evaluated by predicting outcomes for test data and comparing them to actual outcomes.
How can a trained model be used in the Power Platform App Maker?
The trained model can be utilized in the Power Platform App Maker by associating it with a field or control in the application, effectively giving that application component AI capabilities.
How is a ‘Performance score’ useful while developing a model in Power Platform App Maker?
A Performance score gives you an insight into how well the model is expected to do when it is used to score entities. The closer the score is to 1, the better the model performance.
Do we need to retrain the model when new data is added?
Yes, it is recommended that the model be retrained when new data is added, so the model is aware of the changes and can adjust its output accordingly.
What is ‘versioning’ in context to training a model in Power Platform App Maker?
‘Versioning’ allows you to keep multiple trained iterations of a model, each version represents a snapshot of a model at the time it was trained.
Can a published model be deleted from Power Platform App Maker?
No, a published model cannot be deleted. You can, however, unpublish the model.
Why is it necessary to configure fields while training a model in Power Platform?
Configuring fields allows you to define which fields the model will use for training and how it will regard and process the information it retrieves.
What does the ‘Review’ step in the Power Platform stand for?
The ‘Review’ step in the training process allows you to inspect the efficacy of the model and review the effectiveness of the predictions it makes.
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