AI-900: What 3 Key Processes Should You Perform Before Deploying an Azure ML Model?

Learn about the critical steps of data preparation, model training, and model evaluation that must be completed prior to deploying an Azure Machine Learning model as a service.

Question

You are planning to deploy an Azure Machine Learning model as a service that will be used by client applications. Which three processes should you perform before you deploy the model?

A. Model Evaluation.
B. Model Training.
C. Data Encryption.
D. Model Retraining
E. Data Preparation.

Answer

A. Model Evaluation.
B. Model Training.
E. Data Preparation.

Explanation

Model Training involves creating the model based on the prepared dataset.

Model Evaluation assesses the model’s performance and ensures it meets the required accuracy and effectiveness before deployment.

Data Preparation is crucial for cleaning and organizing the data to be used in training the model.

Before deploying an Azure Machine Learning model as a service to be used by client applications, you should perform the following three key processes:

1. Data Preparation (E): This involves cleaning, transforming, and formatting the input data that will be used to train the machine learning model. Proper data preparation is critical to ensure high-quality training data and optimal model performance.
2. Model Training (B): In this step, you use the prepared training data to train the machine learning model. This involves selecting an appropriate algorithm, splitting the data into training and validation sets, and iteratively optimizing the model’s parameters to minimize the training loss and improve its ability to generalize to new, unseen data.
3. Model Evaluation (A): After training, you must thoroughly evaluate the model’s performance using held-out test data to assess metrics like accuracy, precision, recall, F1 score, ROC curves, etc. depending on if it is a classification or regression problem. Model evaluation allows you to validate that the trained model meets the desired performance criteria before deploying it into production.

The other two options, Data Encryption (C) and Model Retraining (D), while important, are not part of the core pre-deployment workflow:

• Data Encryption is a security best practice that should be applied to sensitive data, but it is not a modeling step per se.
• Model Retraining happens post-deployment to fine-tune model performance using new data, so it occurs after the initial development process.

In summary, data preparation, model training, and model evaluation are the three essential processes to complete before deploying an Azure Machine Learning model to ensure it is accurate, reliable and ready for production use cases.

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