Identify Core Tasks in Creating a Machine Learning Solution – Part of AI-900 Exam Topics
To create a machine learning solution, it’s essential to understand how machine learning works on Azure. Azure provides various tools and services, such as Azure Machine Learning and Azure Marketplace, that facilitate the development, training, and deployment of machine learning models. Understanding these tools and their capabilities is crucial for leveraging Azure’s infrastructure and services to build robust machine learning solutions.
Azure Machine Learning and Studio
Azure Machine Learning is a comprehensive platform for managing the end-to-end machine learning lifecycle. Azure Machine Learning Studio is a visual interface that simplifies the process of building, training, and deploying machine learning models. It provides an intuitive environment for data scientists and developers to collaborate on machine learning projects, making it easier to manage and scale their models.
Compute Instances and Data Preparation
Compute instances in Azure Machine Learning provide the necessary computational power to run machine learning experiments. These instances can be scaled according to the requirements of the project. Data ingestion and preparation are critical steps in the machine learning pipeline. Datastores in Azure Machine Learning allow for seamless data storage and management. Importing data from various sources and preparing it for training ensures that the model receives clean and relevant data, which is essential for accurate predictions.
Feature Selection, Engineering, and Model Training
Feature selection and engineering involve identifying the most relevant features from the dataset and transforming them into formats suitable for model training. This step is crucial for improving the model’s performance and accuracy. Model training involves using the prepared data to train machine learning algorithms. Azure Machine Learning provides compute clusters to handle the computationally intensive task of training models on large datasets. Evaluating the model’s performance during and after training ensures that it meets the desired accuracy and reliability.
Model Deployment and Management
After training and evaluating the model, it needs to be deployed so that it can be used in production environments. Model deployment involves making the trained model available as a service that can be accessed by applications to make predictions. Azure Machine Learning provides tools for deploying models, managing their versions, and monitoring their performance. This ensures that the models remain reliable and can be updated as needed to maintain their accuracy and effectiveness.
Identify core tasks in creating a machine learning solution related questions are below.
HOTSPOT
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
