AptCom has commissioned a project to predict real estate pricing.
A tool is used to train and deploy a machine learning model that predicts the price of real estate.
It is to be an “Azure Machine Learning designer.” Below are the steps that will be performed to train the ML model and eventually submit the pipeline.
But the steps are not ordered properly.
Order the steps in the correct sequence so that the ML model is trained and the pipeline is submitted correctly.
Step 1: Prepare data Step 2: Submit the pipeline Step 3: Train a machine learning model Step 4: Create a new pipeline Step 5: Import data Step 6: Set the default compute target.
Click on the arrows to vote for the correct answer
A. B. C. D.Correct Answer:A.
Option A is CORRECT.
The correct sequence is.
Step 1: Create a new pipeline
Step 2: Set the default compute target
Step 3: Import data
Step 4: Prepare data
Step 5: Train a machine learning model
Step 6: Submit the pipeline Option B is INCORRECT.
The 2nd and 3rd steps are incorrectly ordered.
Option C is INCORRECT.
The 4th and 5th steps are incorrectly ordered.
Option D is INCORRECT.
The 2nd and 5th steps are incorrectly ordered.
Reference:
Sure, I'd be happy to explain the correct order of the steps to train the ML model and submit the pipeline in Azure Machine Learning designer.
The correct order of the steps is:
C. Step 4 --> Step 6 --> Step 5 --> Step 3 --> Step 1 --> Step 2
Here's a detailed explanation of each step in the correct order:
Step 4: Create a new pipeline The first step is to create a new pipeline. This pipeline will be used to train and deploy the machine learning model. In Azure Machine Learning designer, a pipeline is a collection of data preparation, training, and deployment steps that can be executed as a single unit.
Step 6: Set the default compute target Before we can train the machine learning model, we need to set the default compute target. A compute target is the compute resource that will be used to run the pipeline steps. In Azure Machine Learning designer, the compute target can be a local machine, a cloud-based compute instance, or a cluster.
Step 5: Import data The next step is to import the real estate data into the workspace. In Azure Machine Learning designer, data can be imported from various sources, such as Azure Blob storage, Azure SQL Database, and others.
Step 3: Train a machine learning model Once the data has been imported, we can start training the machine learning model. In Azure Machine Learning designer, there are several built-in algorithms that can be used to train the model. Additionally, custom models can also be used if needed.
Step 1: Prepare data Before training the machine learning model, it is essential to prepare the data. Data preparation includes activities such as cleaning, transforming, and feature engineering. In Azure Machine Learning designer, data preparation can be done using various modules such as Select Columns, Clean Missing Data, and more.
Step 2: Submit the pipeline Finally, we can submit the pipeline to run the training and deployment steps. In Azure Machine Learning designer, the pipeline can be submitted to the compute target that was set in Step 6. Once the pipeline is submitted, Azure Machine Learning will automatically manage the execution of the pipeline and provide insights into the performance of the model.
Therefore, the correct order of the steps to train the ML model and submit the pipeline in Azure Machine Learning designer is: Step 4 --> Step 6 --> Step 5 --> Step 3 --> Step 1 --> Step 2 (Option C)