Using Azure Cosmos DB for Building, Testing, and Deploying Predictive Analytics
Question
Note: The question is included in a number of questions that depicts the identical set-up. However, every question has a distinctive result. Establish if the solution satisfies the requirements.
You are required to deploy an Artificial Intelligence (AI) solution in Azure.
You want to make sure that you are able to build, test, and deploy predictive analytics for the solution.
Solution: You should make use of Azure Cosmos DB.
Does the solution meet the goal?
Answers
Explanations
Click on the arrows to vote for the correct answer
A. B.B
The given solution suggests using Azure Cosmos DB for deploying an AI solution and enabling the building, testing, and deployment of predictive analytics. However, Azure Cosmos DB is not specifically designed or optimized for AI model training, testing, and deployment. Azure Cosmos DB is a globally distributed, multi-model database service that can store and retrieve data with low latency and high throughput. It provides NoSQL database capabilities and can be used for storing and querying large amounts of structured or semi-structured data.
While Azure Cosmos DB can be integrated into an AI solution as a data storage component to store and retrieve input data or model predictions, it does not provide the necessary tools and capabilities for training and deploying AI models. To build, test, and deploy predictive analytics for an AI solution, you would typically require specialized services such as Azure Machine Learning.
Azure Machine Learning is a cloud-based service that provides a comprehensive set of tools and services for building, training, testing, and deploying machine learning models. It offers a range of capabilities, including data preparation, model training, hyperparameter tuning, model evaluation, and deployment to various endpoints. Azure Machine Learning integrates well with other Azure services, such as Azure Databricks for big data processing, Azure Cognitive Services for pre-built AI models, and Azure Kubernetes Service (AKS) for scalable deployment.
Therefore, the given solution of using Azure Cosmos DB alone does not meet the goal of building, testing, and deploying predictive analytics for an AI solution. The correct answer would be:
B. No
No, the solution does not meet the goal of building, testing, and deploying predictive analytics for the AI solution using Azure Cosmos DB.
Azure Cosmos DB is a globally distributed, multi-model database service that supports document, key-value, graph, and column-family data models. While it can be used for storing and querying large volumes of data, it is not specifically designed for building, testing, and deploying predictive analytics models.
To build, test, and deploy predictive analytics models in Azure, the recommended services are Azure Machine Learning and Azure Databricks. Azure Machine Learning provides a cloud-based environment for developing, training, and deploying machine learning models, while Azure Databricks is a fast, easy, and collaborative Apache Spark-based analytics platform for data engineering, machine learning, and analytics.
Therefore, the correct answer is B. No, the solution does not meet the goal.
