What should you do?

You are a data scientist at an industrial equipment manufacturing company. You are developing a regression model to estimate the power consumption in the company’s manufacturing plants based on sensor data collected from all of the plants. The sensors collect tens of millions of records every day. You need to schedule daily training runs for your model that use all the data collected up to the current date. You want your model to scale smoothly and require minimal development work.

What should you do?
A . Develop a custom TensorFlow regression model, and optimize it using Vertex Al Training.
B. Develop a regression model using BigQuery ML.
C. Develop a custom scikit-learn regression model, and optimize it using Vertex Al Training
D. Develop a custom PyTorch regression model, and optimize it using Vertex Al Training

Answer: B

Explanation:

BigQuery ML is a powerful tool that allows you to build and deploy machine learning models directly within BigQuery, Google’s fully-managed, serverless data warehouse. It allows you to create regression models using SQL, which is a familiar and easy-to-use language for many data scientists. It also allows you to scale smoothly and require minimal development work since you don’t have to worry about cluster management and it’s fully-managed by Google.

BigQuery ML also allows you to run your training on the same data where it’s stored, this will minimize data movement, and thus minimize cost and time.

References:

✑ BigQuery ML

✑ BigQuery ML for regression

✑ BigQuery ML for scalability