BigQuery ML vs Vertex AI: What are the pros and cons

BigQuery ML and Vertex AI are both Google Cloud Platform services that enable users to build, train, and deploy machine learning models. However, they have different use cases, capabilities, and limitations. Below is a brief comparison of the two services:

BigQuery ML:

• BigQuery ML is designed to build and train machine learning models within the BigQuery environment, specifically using SQL-like queries.

• Suitable for users with SQL knowledge who want to build simple machine learning models without requiring extensive knowledge of machine learning frameworks or programming languages.

• Supports a limited set of machine learning algorithms, such as linear regression, logistic regression, k-means clustering, matrix factorization, and time series forecasting.

• Models can be easily retrained using scheduled queries.

• Direct integration with BigQuery allows for easy data access and preprocessing using SQL.

Pros:

1. Easy to use for those familiar with SQL, with minimal learning curve.

2. Integrated directly with BigQuery, allowing seamless access to data and SQL-based data preprocessing.

3. Simplifies the process of training and deploying models.

4. Supports retraining models using scheduled queries.

Cons:

1. Limited to a small set of algorithms and does not support advanced machine learning or deep learning techniques.

2. Less flexible compared to more comprehensive machine learning platforms, such as Vertex AI.

3. Limited support for custom feature engineering or preprocessing pipelines.

Vertex AI:

• Vertex AI is a comprehensive, end-to-end platform for building, training, and deploying machine learning models.

• Supports a wide range of machine learning and deep learning frameworks, such as TensorFlow, PyTorch, and scikit-learn.

• Provides tools for data preprocessing, feature engineering, model evaluation, and hyperparameter tuning.

• Offers AutoML capabilities for automatic model training and selection.

• Supports custom training pipelines and advanced deployment options.

Pros:

1. Supports a wide variety of machine learning and deep learning models, providing more flexibility and customization.

2. Provides advanced tools for data preprocessing, feature engineering, model evaluation, and hyperparameter tuning.

3. Offers AutoML capabilities for users who want to quickly train and deploy models without requiring extensive machine learning expertise.

4. Supports custom training pipelines, allowing for more complex workflows and better integration with external systems.

Cons:

1. Has a steeper learning curve compared to BigQuery ML, as it requires familiarity with machine learning frameworks and programming languages.

2. May require more setup and configuration, as it is not directly integrated with BigQuery.

In summary, BigQuery ML is better suited for users who want to build simple machine learning models using SQL within the BigQuery environment. In contrast, Vertex AI is a more comprehensive machine learning platform that provides greater flexibility, supports a wider range of algorithms, and offers advanced tools for model building, training, and deployment. The choice between the two depends on your specific use case, requirements, and familiarity with machine learning techniques and programming languages.