Machine Learning in Google BigQuery

Google BigQuery allows interactive analysis of large datasets, making it easy for businesses to share meaningful insights and develop solutions based on customer analytics. However, many of the businesses that are using BigQuery aren’t using machine learning to help better understand the data they are generating. This is because data analysts, proficient in SQL, may not have the traditional data science background needed to apply machine learning techniques.

Today we’re announcing BigQuery ML, a capability inside BigQuery that allows data scientists and analysts to build and deploy machine learning models on massive structured or semi-structured datasets. BigQuery ML is a set of simple SQL language extensions which enables users to utilize popular ML capabilities, performing predictive analytics like forecasting sales and creating customer segmentations right at the source, where they already store their data. BigQuery ML additionally sets smart defaults automatically and takes care of data transformation, leading to a seamless and easy to use experience with great results.
When designing the BigQuery ML backend, the team was faced with a dilemma. Transferring large amounts of data from BigQuery servers to special-purpose servers running machine learning algorithms would be time-consuming and would incur an overhead in terms of security and privacy considerations. However, because the core components of gradient descent — an optimization method that is the workhorse of machine learning algorithms — can be implemented using common SQL operations*, we were able to repurpose the existing BigQuery SQL processing engine for BigQuery ML.

Since the BigQuery engine is designed to efficiently scan large datasets rather than randomly draw small samples from them, BigQuery ML is based on the standard (batch) variant of gradient descent rather than the stochastic version. And while stochastic gradient descent is far more common in today’s large-scale machine learning systems, the batch variant has numerous practical advantages.

For example, in-database machine learning systems based on stochastic gradient descent process examples one by one, and can perform poorly when the data is suboptimally ordered. But BigQuery data is often distributed on disk so as to optimize the performance of regular SQL queries, and continually redistributing the data to support stochastic machine learning algorithms would be computationally expensive. In contrast, batch gradient descent is insensitive to the ordering and partitioning of data on disk, thereby completely circumventing this problem. Also, batch methods can be combined with line search techniques from the classical optimization literature, leading to a learning algorithm that is more stable and requires less fine tuning. Using line search with stochastic methods is far trickier. Our implementation also includes support for regularization and preconditioning. For more details, please see our paper.

We hope that you’ll find BigQuery ML useful for many predictive analytics tasks. To try it, visit the BigQuery console and follow the user guide. Creating a model is as simple as:
CREATE MODEL dataset.model_name
OPTIONS(model_type=’linear_reg’, input_label_cols=[‘input_label’])
AS SELECT * FROM input_table;
In the future, we plan to further integrate our gradient descent implementation with BigQuery infrastructure to realize more performance gains. We’re also going to explore other machine learning algorithms that can be easily and efficiently implemented for large-scale problems by leveraging the power of BigQuery.

BigQuery ML is the result of a large collaboration across many teams at Google. Key contributors and sponsors include Hossein Ahmadi, Corinna Cortes, Grzegorz Czajkowski, JD Degenaar, Dan Delorey, Mingge Deng, Danielle Hanks, Amir Hormati, Abhishek Kashyap, Jing Jing Long, Dan McClary, Chris Meyers, Ross Popoff-Walker, Girishkumar Sabhnani, Vivek Sharma, Jordan Tigani, Chad Verbowski, Jiaxun Wu and Lisa Yin.

* For example, a gradient vector can be computed using the SUM and GROUP BY operators, and the weights of a model can be updated using an INNER JOIN.