Exam Cloud Digital Leader topic 1 question 207 discussion

C: BigQuery ML enables users with SQL skills to create and execute machine learning models directly within Google BigQuery. This approach is particularly suitable for organizations like the one described, which may not have specialized machine learning expertise but does have database administration skills. BigQuery ML allows these users to leverage their existing SQL knowledge to build and train predictive models, making it an accessible option for generating insights from their data.

C. Use BigQuery ML and create models using SQL. BigQuery ML is the best choice for organizations without a data science team but with in-house database administration skills. Here's why: - SQL-based modeling: BigQuery ML allows users to create and deploy machine learning models using simple SQL queries, which database administrators are already familiar with. - Ease of use: It eliminates the need to learn programming languages like Python or frameworks like TensorFlow. - Integrated with BigQuery: It works directly with data stored in BigQuery, making it easy to analyze and predict trends like property popularity. - Quick setup: Organizations can rapidly build, train, and deploy models without specialized expertise.