Creating an AI Builder model in the Microsoft Power Platform involves understanding and executing a particular lifecycle. This lifecycle is a chronological sequence of five fundamental steps. These steps include choosing an AI model type, collecting and preparing data, training and publishing the model, and finally using and monitoring the model.
1. Choose the AI Model Type:
The first step in the AI Builder model’s lifecycle is identifying the model type you want to deploy. The model type selection will heavily rely on your business requirements. In the Microsoft Power Platform, you have the option to choose from several prebuilt AI models such as prediction, form processing, object detection, or category classification.
2. Collect and Prepare the Data:
The second step centers around data collection and preparation. This step is crucial because the quality of your AI model largely depends on the quality of your data. Therefore, adequate preparation and cleaning should be carried out to ensure that the data is free from errors and inconsistencies. The collected data should represent the scenarios where the model will be used. Also, the data should be tagged appropriately for training. SharePoint, Excel, Dataverse, and more data sources are supported for AI Builder models.
3. Train the Model:
Once the data has been collected and prepared, the next step involves training the AI model. Training involves feeding the prepared data into the AI model, allowing the model to learn various patterns and trends within the data. The duration of the training process depends on the size and complexity of the data.
4. Publish the Model:
After the training is complete and the model produces acceptable results, the next step is to publish the model. Publishing the model essentially means making the model available for use in the Power Platform environment.
5. Use and Monitor the Model:
The final step is to incorporate the AI model into applications, automations, and analytics. Monitor the model usage, and retrain it with new data as needed over time. Monitoring the model is crucial to ensure its effectiveness and make necessary adjustments.
Example of the lifecycle in practice:
If you want to predict if a customer will purchase a product in the next month. You would select the prediction model. You then collect and prepare your customer data, which could include details like the customer’s buying history, browsing data, demographic data, and more. The data needs to be cleaned, and any anomalies should be removed to improve training efficiency.
The model can then be trained on this data using the AI Builder in the Power Platform. After training, the model will be able to discern patterns and make predictions based on the input data. Once satisfactory results are obtained, the model can be published, making it available for use across applications within the Power Platform environment. This published model can predict the chance of a customer buying a product based on their input characteristics.
Lastly, the model’s accuracy and effectiveness should be monitored regularly. Retraining the model with updated or new data helps improve its predictive capabilities and keep it relevant.
This lifecycle of creating an AI Builder model is an iterative process. With continual data collection, training, publishing, and monitoring, the AI model will become ever more refined, and its predictions more accurate.
Microsoft Power Platform Fundamentals Exam (PL-900)
Understanding this process thoroughly is important, especially for the PL-900 Microsoft Power Platform Fundamentals exam. This exam, among other things, tests your knowledge on understanding, deploying, and managing AI Builder models.
Practice Test
True or False: AI Builder is a platform provided by Microsoft that allows users to train, build and deploy AI models.
- True
- False
Answer: True
Explanation: AI Builder is a cloud-based platform provided by Microsoft that lets users train, build and deploy artificial intelligence models.
True or False: AI Builder model lifecycle only includes two stages, which are configuring the model and training the model.
- True
- False
Answer: False
Explanation: The lifecycle of an AI Builder model typically includes multiple stages such as planning, configuring the model, training the model, evaluating the model and eventually deploying the model.
Which of the following is the first step in the AI Builder model lifecycle?
- a) Training the model
- b) Configuring the model
- c) Evaluating the model
- d) Planning
Answer: d) Planning
Explanation: Planning is typically the first step in creating an AI model with Microsoft’s AI Builder. It includes defining the problem to be solved and collecting and preparing the data.
True or False: You cannot use Power Automate to predict and process data with the AI model built on AI Builder.
- True
- False
Answer: False
Explanation: You can utilize Power Automate or Power Apps to use the predictive results and process data with the built AI model.
In which stage of the AI Builder model lifecycle do we use the data to let the model learn and improve?
- a) Planning
- b) Configuring the model
- c) Training the model
- d) Deployment
Answer: c) Training the model
Explanation: During the training phase, the model is exposed to data to learn patterns and improve its predictive capabilities.
Multiple Choice: What is the purpose of the ‘Evaluating the model’ stage in AI Builder model life cycle?
- a) To test the accuracy of the AI model
- b) To deploy the AI model
- c) To plan for the AI model
- d) None of the above
Answer: a) To test the accuracy of the AI model
Explanation: The evaluation stage is intended to test the precision and accuracy of the AI model before deploying it.
True or False: The user doesn’t require any knowledge of machine learning to use AI Builder.
- True
- False
Answer: True
Explanation: AI Builder is designed to enable professionals without machine learning expertise to harness the power of AI.
True or False: AI Builder can only be used to create new AI models and not for enhancing existing models.
- True
- False
Answer: False
Explanation: AI Builder can be used for creating new AI models as well as enhancing functionality and accuracy of existing models.
Which Microsoft Power Platform tool can be used to design and build custom business apps?
- a) Power Automate
- b) Power Apps
- c) Power BI
- d) Power FX
Answer: b) Power Apps
Explanation: Power Apps is a tool within Microsoft Power Platform specifically designed to allow users to build and customize business apps.
What is the final stage in the AI Builder model lifecycle?
- a) Training
- b) Reviewing
- c) Deploying
- d) Evaluating
Answer: c) Deploying
Explanation: Deploying the AI model is typically the final stage in the AI Builder model lifecycle after the evaluation stage has been satisfactorily completed.
Interview Questions
What is the first step in creating an AI Builder model?
The first step is specifying what type of AI model you want to build based on the tasks you want it to perform.
What’s the second step to creating an AI Builder model after specifying the type of AI model?
The second step is to choose the training data. The AI model needs to be trained using quality labeled data to ensure accuracy in predictions.
After choosing the training data, what is the next step in creating an AI Builder model?
After choosing the training data, the next step is to train the AI model. This includes teaching the model using training data and making adjustments until it can predict accurate outcomes.
What happens after the AI model has been trained?
After training the model, the next step is to publish the AI model. Publishing makes it available for use in Power Apps, Power Automate, or other Power Platform applications.
What’s the final step in the life cycle of creating an AI Builder model?
The final step is to use the published model in an application. This could be in Power Apps, Power Automate or any other application that supports AI Builder models.
Can you retrain the AI Builder model after it has been published and used in applications?
Yes, the AI Builder model can be retrained after it has been published and used in applications to improve the accuracy of its predictions if they are not satisfactory.
What kind of data is required for training the AI Builder model?
Labeled data is required for training the AI Builder model, which means each example data point must be associated with a correct answer or solution.
What happens if the AI Builder model isn’t accurate enough after the first round of training?
If the model isn’t accurate enough after the first round of training, it can be retrained with additional or new data to improve its accuracy.
Does the AI Builder model need to be re-published after being retrained?
Yes, it needs to be re-published before the retrained version can be accessed by applications.
Does using the AI Builder model require any specific software or programming language skills?
No, one of the key advantages of the AI Builder is its accessibility. It integrates with Microsoft’s Power Platform, which has a user-friendly, no-code interface.
Can the AI Builder model be used in different types of applications?
Yes, once published, the AI Builder model can be used in many types of applications including Power Apps, Power Automate and even custom applications through the use of API.
Does the AI Builder model need any maintenance once it’s been published and implemented in applications?
The AI Builder model may need occasional retraining to stay accurate if the data trends shift or change over time.
What are some examples of AI models that can be created using AI Builder?
Some examples of AI models that can be created using AI Builder include prediction, object detection, form processing, text classification and entity extraction models.
Can you use data from external sources to train the AI Builder model?
Yes, the AI Builder can use data from many external sources, such as Azure SQL Database and SharePoint, for training as long it is structured and labeled.
What is one of the key considerations while choosing the training data for AI Builder?
One key consideration while choosing training data for AI Builder is to ensure it is representative of the situations the AI model will encounter once it’s deployed. This helps in making accurate predictions.
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