Understanding BigQuery ML and its Benefits

Businesses generate a wealth of data each day, but only a few adopt machine learning (ML) solutions and understand its benefits. This is, in part, due to scarce expertise with ML and the time and computing power needed to run quick and effective models. Additionally, the challenge of managing the volume and speed of data continues to increase exponentially, which leaves business leaders scrambling to find a way to capitalize on ML.

Predictive analytics is becoming increasingly important for data analysts. As a result, a modern data warehouse should support machine learning straight from where it’s stored.

With its introduction in 2018, BigQuery ML has been a key tool to assist data scientists and analysts. BigQuery ML allows them to build and operationalize machine learning models, directly within BigQuery, using simple SQL. In essence, BigQuery ML democratizes data science capabilities for data analysts with a basic understanding of ML, allowing analysts to execute projects with ease and reducing dependencies on their lean data science team. 

Let’s take a look at some of the benefits of the built-in artificial intelligence and machine learning capabilities of BigQuery ML.

A major benefit of BigQuery as a modern data warehouse is its fully managed, scalable infrastructure. This benefit also carries over to BigQuery ML.

Building massive scalable robust machine learning pipelines can be done with many different tools today but is often complex and difficult to scale. That, coupled with the increasing amount of data gathered by organizations daily, can prove to be a challenge. 

Organizations have the ability to run models over entire live databases. For example, performing ad hoc at-scale machine learning is possible as part of day-to-day operations, rather than as a special project. Traditional processes of small, stale data extracts can be avoided entirely as the scalability of BigQuery ML negates that challenge. 

Time is a valuable resource to businesses, especially in today’s ever-changing marketplace. Therefore, it is important to consider the benefits that BigQuery ML brings in delivering ML capabilities without having to leave the data warehouse.

The need to export and reformat data has the following disadvantages:

• Increases complexity because multiple tools are required.
• Reduces speed because moving and formatting large amounts of data for Python-based ML frameworks takes longer than model training in BigQuery.
• Requires multiple steps to export data from the warehouse, restricting the ability to experiment on your data.
• Can be prevented by legal restrictions such as HIPAA guidelines.

BigQuery ML increases the speed of model development and innovation by removing the need to export data from the data warehouse. Instead, BigQuery ML brings ML to the data itself.

With BigQuery ML, there is no need to learn a more advanced programming language like Python or Java to set up the model. Instead, users can use the basic language that data analysts already know – SQL. 

BigQuery ML democratizes the use of ML to be accessible to more users, freeing up resources while empowering analysts to build and run models using existing business intelligence tools and spreadsheets. 

This reduced dependency on other team members, especially data scientists, allows enterprises to increase development speed without the issue of only a few in the company knowing advanced programming languages. Let’s face it, hiring a team of data scientists to build predictive analytics solutions is costly. Instead, BigQuery ML helps to democratize the ability to build effective models.

Predictive analytics has become an integral part of digital transformation. Capitalizing on the benefits that artificial intelligence and machine learning provide allows companies to reap the huge benefits of making data-driven decisions each and every day.

With the power that BigQuery and BigQuery ML provide, organizations can further democratize their data science capabilities right from the data warehouse itself. Coupled with the power of a highly scalable modern data warehouse, BigQuery ML can be utilized for running models over real-time datasets without the need for exporting or other ML tools.

The embedded machine learning benefits of BigQuery ML cannot be understated. For enterprises looking to empower their data analysts and scientists and accelerate their ML journey, look no further than BigQuery ML.

Implementing new technologies and solutions can seem daunting. However, Pandera’s data science experts can guide you in better understanding your customers, personalizing the customer experience, automating, as well as improving business processes. 

Get in touch with us today to connect with our Google Cloud AI experts to learn about what solution works best for your AI/ML business needs!