Which Machine Learning framework should a user with SQL knowledge and little Machine Learning experience use for a 'Low-Code' solution?

Which Machine Learning framework should a user with SQL knowledge and little Machine Learning experience use for a 'Low-Code' solution?

• A. TensorFlow
• B. Big Query ML
• C. PyTorch
• D. Scikit-learn

Answer: (B)

BigQuery ML is an ideal choice for users who have SQL knowledge but limited experience with machine learning. It allows data analysts and other users to create and execute machine learning models directly within the familiar SQL syntax, making it accessible without requiring deep knowledge of the underlying complexities of machine learning algorithms or frameworks. This platform leverages the power of Google BigQuery, enabling users to utilize their existing SQL skills effectively to manage and analyze large datasets while also training and deploying models. Users can easily perform tasks such as regression, classification, and time-series forecasting using SQL queries, providing a user-friendly interface that abstracts much of the complexity involved in traditional machine learning workflows. Other frameworks like TensorFlow, PyTorch, and Scikit-learn, while powerful and versatile, generally require a stronger programming background and familiarity with machine learning concepts. They may not cater as directly to users who are primarily comfortable with SQL, making them less optimal for those seeking a low-code solution.

BigQuery ML is an ideal choice for users who have SQL knowledge but limited experience with machine learning. It allows data analysts and other users to create and execute machine learning models directly within the familiar SQL syntax, making it accessible without requiring deep knowledge of the underlying complexities of machine learning algorithms or frameworks.

This platform leverages the power of Google BigQuery, enabling users to utilize their existing SQL skills effectively to manage and analyze large datasets while also training and deploying models. Users can easily perform tasks such as regression, classification, and time-series forecasting using SQL queries, providing a user-friendly interface that abstracts much of the complexity involved in traditional machine learning workflows.

Other frameworks like TensorFlow, PyTorch, and Scikit-learn, while powerful and versatile, generally require a stronger programming background and familiarity with machine learning concepts. They may not cater as directly to users who are primarily comfortable with SQL, making them less optimal for those seeking a low-code solution.